Showing 1–50 of 113 results for author: Meng, X

Ion-Implanted Silicon Nanoregions Enable Ultra-Low-Loss Trimming of Cladded Photonic Integrated Circuits

Authors: Zhongyu Tang, Shabnam Taheriniya, Seongmin Jo, Xinyu Ma, Akhil Varri, Anna P. Ovvyan, Vincent Spreter, Liam McRae, Xiansong Meng, Wolfram H. P. Pernice, Rongyang Xu

Abstract: Photonic integrated circuits (PICs) have emerged as a key platform for information processing, including optical communication and computing. As PIC complexity increases, fabrication-induced response deviations accumulate, making post-fabrication trimming critical for unlocking their full potential. Here, we demonstrate that silicon ion implantation enables scalable, ultra-low-loss trimming of cla… ▽ More Photonic integrated circuits (PICs) have emerged as a key platform for information processing, including optical communication and computing. As PIC complexity increases, fabrication-induced response deviations accumulate, making post-fabrication trimming critical for unlocking their full potential. Here, we demonstrate that silicon ion implantation enables scalable, ultra-low-loss trimming of cladded PICs by locally forming a high-index silicon-rich region. Structural characterization confirms that the silicon-implanted nanoregion is confined to the cladding without observable damage to the underlying waveguide. The implantation-induced excess loss is below 0.001 dB per π phase shift, while the optical response remains stable over a four-month observation period. Automated trimming is demonstrated on a photonic crossbar array, reducing the average channel output variation from 78.5% to 5.9%. These results establish a practical route towards automated, ultra-low-loss post-fabrication trimming for large-scale cladded PICs. △ Less

Submitted 9 July, 2026; originally announced July 2026.

Comments: 14 pages, 7 figures

Monte Carlo Physics-informed Neural Networks for Inverse Multiscale Heat Conduction Problems via the Phonon Boltzmann Transport Equation

Authors: Qingyi Lin, Chuang Zhang, Xuhui Meng, Zhaoli Guo

Abstract: Inferring thermal fields and thermophysical properties from limited measurements is a fundamental challenge in micro- and nanoscale heat conduction, where the classical Fourier law breaks down and the phonon Boltzmann transport equation (BTE) is needed to capture non-diffusive transport effects. In this work, we extend Monte Carlo physics-informed neural networks (MC-PINNs), originally developed f… ▽ More Inferring thermal fields and thermophysical properties from limited measurements is a fundamental challenge in micro- and nanoscale heat conduction, where the classical Fourier law breaks down and the phonon Boltzmann transport equation (BTE) is needed to capture non-diffusive transport effects. In this work, we extend Monte Carlo physics-informed neural networks (MC-PINNs), originally developed for forward phonon BTE problems [J. Comput. Phys. 542, 114364, 2025], to inverse multiscale heat conduction problems. Two representative classes of inverse problems are considered: (i) reconstructing the full thermal field from sparse interior temperature measurements when boundary conditions are unknown, and (ii) simultaneously inferring the unknown relaxation time together with the thermal field. Problem-specific MC-PINN architectures and training strategies are designed for each class. The mesh-free Monte Carlo sampling strategy enables a unified treatment across diffusive, transitional, and ballistic transport regimes without requiring a priori knowledge of the relaxation time. The proposed method is evaluated on quasi-one-dimensional, quasi-two-dimensional, and three-dimensional benchmark problems covering a wide range of Knudsen numbers, as well as on a realistic 3D fin field-effect transistor (FinFET) structure. Results demonstrate that MC-PINNs consistently outperform purely data-driven deep neural networks, particularly in the sparse-data regime, and can accurately infer spatially uniform relaxation times. For spatially varying relaxation times, the inferred distributions capture the dominant thermal response, and numerical simulations using the recovered parameters reproduce the macroscopic fields with good accuracy. These findings establish MC-PINNs as an effective and physically consistent framework for inverse thermal analysis at micro- and nanoscales. △ Less

Submitted 1 July, 2026; v1 submitted 24 June, 2026; originally announced June 2026.

HoloPathTracer: Fast and Accurate Wave Path Tracing for Holography

Authors: Wenbin Zhou, Xiangyu Meng, Jiankai Xing, Xin Liu, Suyeon Choi, Yifan Peng

Abstract: Holography offers unique advantages for delivering perceptual realism while preserving compact form factors in VR/AR. Its perceptual quality, however, hinges on encoding rich wavefronts of photorealistic scenes into interference patterns and then incoherently multiplexing the resulting wave fields for perception. Existing CGH paradigms decouple radiance estimation from wave propagation by pre-rend… ▽ More Holography offers unique advantages for delivering perceptual realism while preserving compact form factors in VR/AR. Its perceptual quality, however, hinges on encoding rich wavefronts of photorealistic scenes into interference patterns and then incoherently multiplexing the resulting wave fields for perception. Existing CGH paradigms decouple radiance estimation from wave propagation by pre-rendering radiance on discretized scene sectors. This separation between radiometric and wave-optical computation inherently limits the range of focus cues and visual effects that can be faithfully reproduced, including depth- and view-continuity, and physically based material behaviors such as glossy or mirror-like reflection and refraction. We present a physically accurate yet computationally efficient wave optics rendering framework leveraging path tracing to encode full 3D visual cues into phase holograms. Specifically, we employ a Monte Carlo method to solve both the rendering equation and the Rayleigh--Sommerfeld integral simultaneously. Our algorithm is fully compatible with modern graphics techniques and can generate multiple time-multiplexed random holograms with minimal additional time cost via Path Reuse. By employing a fast approximation with an ambient radiance cache, we realize an order of magnitude convergence speed improvement. The resulting coherent wave fields that inherently encode comprehensive visual effects are converted into phase-only holograms under complex-amplitude supervision. Through extensive simulations and experimental validations on a spatial light modulator-based display prototype, we demonstrate faithful holographic reconstructions of natural 3D cues and complex materials, including realistic defocus blur, view-dependent effects, as well as appearance highlights and reflections. △ Less

Submitted 16 June, 2026; v1 submitted 12 June, 2026; originally announced June 2026.

Comments: ACM Transactions on Graphics, Vol. 45, No. 4, Article 39, July 2026, 12 pages. Presented at SIGGRAPH 2026

ACM Class: I.3.7; I.3.3

Journal ref: ACM Trans. Graph. 45, 4, Article 39 (July 2026), 12 pages

Flow-based generative models for amortized Bayesian inference in regression and inverse PDE problems

Authors: Shaoqian Zhou, Ling Guo, Xuhui Meng

Abstract: Bayesian inference provides a principled framework for uncertainty quantification in scientific machine learning. However, conventional Bayesian approaches usually require solving a new inference problem for each observation set, causing substantial computational costs that hinder real-time applications like online monitoring and digital twins. Furthermore, inferring over infinite-dimensional func… ▽ More Bayesian inference provides a principled framework for uncertainty quantification in scientific machine learning. However, conventional Bayesian approaches usually require solving a new inference problem for each observation set, causing substantial computational costs that hinder real-time applications like online monitoring and digital twins. Furthermore, inferring over infinite-dimensional function spaces with varying observation sets poses major challenges for existing amortized inference methods. In this work, we propose Flow-ABI, a flow-based generative framework for amortized Bayesian inference in regression and inverse partial differential equation (PDE) problems. It consists of two components: (i) a functional prior model that learns expressive priors from historical data and physical knowledge through flow matching, and (ii) a set-conditioned functional posterior sampler mapping observation sets to functional posterior distributions. The learned posterior model naturally accommodates varying, permutation-invariant observation sets, and generalizes across different observation discretizations. Once trained, Flow-ABI enables near-real-time posterior sampling for previously unseen observations without retraining or iterative optimization. The proposed methodology can be seamlessly integrated with a wide class of scientific machine learning frameworks, including physics-informed neural networks and neural operators, for uncertainty-aware inverse PDE modeling. Experiments demonstrate that Flow-ABI accurately captures both Gaussian and non-Gaussian posterior distributions while achieving over two-order-of-magnitude speedups relative to the gold-standard Bayesian inference method, Hamiltonian Monte Carlo. These results show Flow-ABI is an effective, scalable, and computationally efficient framework for uncertainty quantification in scientific machine learning. △ Less

Submitted 8 June, 2026; originally announced June 2026.

Interface-dependent Phase Transitions and Ultrafast Hydrogen Superionic Diffusion of H2O Ice

Authors: Pengfei Hou, Yumiao Tian, Zifeng Liu, Junwen Duan, Hanyu Liu, Xing Meng, Russell J. Hemley, Yanming Ma

Abstract: High-pressure experiments using diamond anvils have revealed novel properties and phase behavior of H2O under extreme conditions. When contained in diamond-anvil cells, the H2O samples are usually in direct contact with the diamond anvil. However, the extent to which this interface affects measured pressure-induced properties and behavior, including coexistence lines of ice phases, remains unknown… ▽ More High-pressure experiments using diamond anvils have revealed novel properties and phase behavior of H2O under extreme conditions. When contained in diamond-anvil cells, the H2O samples are usually in direct contact with the diamond anvil. However, the extent to which this interface affects measured pressure-induced properties and behavior, including coexistence lines of ice phases, remains unknown. Combining artificial neural network methods and active learning schemes with large-scale molecular dynamics simulations, we elucidate the interfacial effects on various properties of high-pressure ice phases, including superionic states, solid-solid phase transitions, and melting. The results reveal that the presence of this interface can significantly lower the hydrogen superionic transition temperature. Remarkably, the interface can also induce a spontaneous transition from bcc- to fcc-based ice following the inverse Bain mechanism. Further, we redefined a stability field of bcc and fcc ice below the melting line and predicted the existence of fcc ice at much lower pressures than previously thought. More broadly, the results emphasize the importance of interface effects in understanding a wide range of phenomena reported in experimental studies of ice under pressure, including inconsistencies between theoretical and experimental results of this fundamental system. △ Less

Submitted 18 March, 2026; originally announced March 2026.

Comments: 30 pages, 21 figures

A simple experiment for observing clustering and dynamics of coalescing particles in air turbulence

Authors: L. Fu, J. Feng, Y. Chen, F. Gong, X. Meng, E. -W. Saw

Abstract: A novel experimental platform is developed to investigate the dynamics of inertial particles (micro-droplets) in air turbulence. The goal is to observe particle collision and coalescence in turbulent flows, focusing on its impact on the radial distribution function (RDF) and relative velocity statistics. The main tool is a three-dimensional Lagrangian particle tracking (LPT) system, designed for h… ▽ More A novel experimental platform is developed to investigate the dynamics of inertial particles (micro-droplets) in air turbulence. The goal is to observe particle collision and coalescence in turbulent flows, focusing on its impact on the radial distribution function (RDF) and relative velocity statistics. The main tool is a three-dimensional Lagrangian particle tracking (LPT) system, designed for high-resolution measurements at sub-Kolmogorov scales. The system uses LED illumination with high-speed spinning-disk atomizers, enabling tracking of particles of approximately 10~$μ$m and larger under controlled turbulence. A minimum resolvable particle separation of $r/η\approx 0.1$ is achieved. A central contribution is the identification and mitigation of three dominant sources of spurious particles: FMIS, IIS, and TIF. An angle-based geometric filtering criterion strongly suppresses FMIS artifacts on RDF. These procedures establish a validated workflow for reliable small-scale statistics. Using this framework, RDF and a normalized pseudo-collision rate are measured at near-contact separations for particles with Stokes numbers $St \approx 0.2$--$1.0$. Sub-Kolmogorov clustering increases with Stokes number, and near-contact statistics are consistent through the filtering strategy. This study extends LPT limits and provides a reliable methodology for investigating inertial-particle dynamics at previously inaccessible spatial scales. △ Less

Submitted 11 March, 2026; v1 submitted 9 March, 2026; originally announced March 2026.

Measurement and Modeling of Structure-Induced Surface Scattering on Terahertz Channel

Authors: Peian Li, Yapeng Ge, Jiacheng Liu, Wenbo Liu, Jiayuan Cui, Jiabiao Zhao, Qiang Niu, Yuping Yang, Xiangzhu Meng, Yiming Zhao, Jianjun Ma

Abstract: As terahertz (THz) frequencies emerge as promising candidates for next-generation wireless networks, accurate characterization of propagation mechanisms in indoor/outdoor environments becomes essential for system design and performance optimization. This article presents an experimental and theoretical investigation of structure-induced indoor surface scattering on THz channels, examining how mate… ▽ More As terahertz (THz) frequencies emerge as promising candidates for next-generation wireless networks, accurate characterization of propagation mechanisms in indoor/outdoor environments becomes essential for system design and performance optimization. This article presents an experimental and theoretical investigation of structure-induced indoor surface scattering on THz channels, examining how material properties and structural configurations jointly govern channel power and angular distribution. Six representative indoor surfaces are characterized, revealing that intrinsic structural inhomogeneity -- particularly the quasi-periodic earlywood-latewood arrangement in pine wood -- induces measurable angular scattering whose dominant lobes and angular shifts are reproduced by a beam-propagation modeling (BPM) framework. Material-covered surface configurations are further investigated, demonstrating that thin dielectric covering layers can substantially modify reflection characteristics through thickness- and frequency- dependent thin-film interference effects. Wide-angle bistatic measurements conducted in a conference-room environment reveal that structured indoor elements, such as folded curtains, can enhance angular scattering and extend spatial coverage. These findings establish that structure-induced surface scattering mechanisms offer potential for constructing non-line-of-sight THz links in indoor environments. △ Less

Submitted 27 February, 2026; originally announced February 2026.

Comments: Submitted to IEEE Transactions on Antenna and Propagation

The effect of graphene orientation on permeability and corrosion initiation under composite coatings

Authors: Majid Shaker, Erfan Salahinejad, Weiqi Cao, Xiaomin Meng, Vahdat Zahedi Asl, Qi Ge

Abstract: The unique anisotropic properties of graphene, particularly impermeability, have made it a promising candidate for further advances in corrosion prevention applications. Despite the large number of experimental works divulging the use of graphene in anticorrosion coatings, there is no report on the numerical modelling and simulation of the relationships between the orientation of graphene sheets i… ▽ More The unique anisotropic properties of graphene, particularly impermeability, have made it a promising candidate for further advances in corrosion prevention applications. Despite the large number of experimental works divulging the use of graphene in anticorrosion coatings, there is no report on the numerical modelling and simulation of the relationships between the orientation of graphene sheets in composite coatings and the introduced corrosion protection efficiency, to our knowledge. Herein, it is tried to model the influence of the orientation of graphene sheets dispersed in organic coatings on the diffusivity and flux of corrosive substances besides the corrosion initiation time of the protected substrates. To discover the relationship between the graphene orientation and corrosion-related phenomena, this study introduces a novel model consisting of a trigonometric factor named unprotected projected surface area proportion, which calculates corrosion-related parameters based on the principal Fick's laws. The model reveals that the decrease in the angle between graphene sheets and the substrate is highly beneficial for postponing the corrosion onset. It is accordingly estimated that a mismatch angle of 10 degree can slow down the diffusion process significantly and delay the corrosion initiation by around 65 times in a 100 micron thick epoxy/graphene composite coating in comparison to the counterpart with the perpendicular alignment. The predicted corrosion parameters were in a good agreement with the experimental data, indicating the merit of the proposed model. Thus, this model can be further employed as the fundamental of future research on the optimum graphene orientation in anticorrosion composite coatings. △ Less

Submitted 2 February, 2026; originally announced February 2026.

Journal ref: Construction and Building Materials, 319 (2022) 126080

Electrical detection of high-order optical orbital angular momentum

Authors: Guanyu Zhang, Xianghan Meng, Zini Cao, Hai Lin, Shuxin Huang, Minghao Deng, Jiaqi Li, Qihuang Gong, Guowei Lyu

Abstract: The orbital angular momentum (OAM) of light provides an unbounded set of orthogonal modes for ultrahigh-capacity optical information processing. However, current OAM detection schemes typically rely on light interference or diffraction, which require bulky optical components and pose a major obstacle to on-chip integration. Here, we demonstrate a fully integrated silicon-based photodetector that e… ▽ More The orbital angular momentum (OAM) of light provides an unbounded set of orthogonal modes for ultrahigh-capacity optical information processing. However, current OAM detection schemes typically rely on light interference or diffraction, which require bulky optical components and pose a major obstacle to on-chip integration. Here, we demonstrate a fully integrated silicon-based photodetector that enables direct electrical detection of light OAM. This photodetector can resolve vortex beams with topological charges from m = -9 to 9, achieving a record-high mode number resolution among on-chip devices. By integrating plasmonic gratings onto the device electrodes, incident vortex beams can be converted into surface plasmon polaritons with OAM-dependent splitting angles, which in turn produce photocurrents that vary monotonically with the OAM order. Further incorporation of a surface dielectric lens can enhance mode resolution, and a split-electrode architecture enables OAM chirality discrimination. Owing to its CMOS-compatibility and spectral scalability, this platform provides a compact and robust solution for integrated OAM detection, opening new opportunities for on-chip optical communication and computing systems based on structured light. △ Less

Submitted 3 February, 2026; v1 submitted 20 January, 2026; originally announced January 2026.

Comments: The language of the abstract and introduction has been optimized; The fonts and color schemes in the images were standardized

Journal ref: Zhang, G. et al. Electrical Detection of High-Order Optical Orbital Angular Momentum. ACS Nano 20, 21686-21694 (2026)

A High-Flux and High-Efficiency Setup for Magneto-Infrared Spectroscopy

Authors: Zeping Shi, Wenbin Wu, Zhiwei Zhang, Yuhan Du, Chenyao Xu, Guangyi Wang, Mingsen Zhou, Congming Hao, Xianghao Meng, Xiangyu Jiang, Chunhui Pan, Wei Lu, Hao Shen, Haifeng Pan, Zhenrong Sun, Junhao Chu, Xiang Yuan

Abstract: We report the design and implementation of a high-flux, high-efficiency magneto-infrared spectroscopy system optimized for broadband measurements in high magnetic fields. The setup integrates a Fourier transform infrared spectrometer, a 12 T cryogen-free superconducting magnet, precision-polished and gold-plated light tubes, custom-designed reflective focusing modules for Faraday and Voigt geometr… ▽ More We report the design and implementation of a high-flux, high-efficiency magneto-infrared spectroscopy system optimized for broadband measurements in high magnetic fields. The setup integrates a Fourier transform infrared spectrometer, a 12 T cryogen-free superconducting magnet, precision-polished and gold-plated light tubes, custom-designed reflective focusing modules for Faraday and Voigt geometries, and an external multi-detector chamber with motorized selection. Optical throughput is maximized by reducing light tube loss from 65.5%/m to 22.0%/m via abrasive flow and mechanical polishing followed by gold electroplating, and by adopting a single-on-axis parabolic-mirror Faraday module that increases the effective numerical aperture from 0.14 to 0.36, enhancing collection efficiency by nearly an order of magnitude. An eight-position motorized sample stage and fully automated control over magnetic field, temperature, optical path, and detector choice enable high-throughput measurements without repeated warm-ups. The optimized configuration achieves a root-mean-square noise level of 0.0061% in a 2-minute integration for a 40% reflectivity sample, corresponding to a signal-to-noise ratio exceeding 16000. System capabilities are demonstrated by resolving weak replica bands in EuCd2As2 and faint Landau level transitions in LaAlSi. △ Less

Submitted 16 December, 2025; originally announced December 2025.

Journal ref: Rev. Sci. Instrum. 96, 113902 (2025)

MuCol Milestone Report No. 7: Consolidated Parameters

Authors: Rebecca Taylor, Antoine Chancé, Dario Augusto Giove, Natalia Milas, Roberto Losito, Donatella Lucchesi, Chris Rogers, Lucio Rossi, Daniel Schulte, Carlotta Accettura, Simon Adrian, Rohit Agarwal, Claudia Ahdida, Chiara Aime, Avni Aksoy, Gian Luigi Alberghi, Simon Albright, Siobhan Alden, Luca Alfonso, Muhammad Ali, Anna Rita Altamura, Nicola Amapane, Kathleen Amm, David Amorim, Paolo Andreetto , et al. (437 additional authors not shown)

Abstract: This document is comprised of a collection of consolidated parameters for the key parts of the muon collider. These consolidated parameters follow on from the October 2024 Preliminary Parameters Report. Attention has been given to a high-level consistent set of baseline parameters throughout all systems of the complex, following a 10 TeV center-of-mass design. Additional details of the designs con… ▽ More This document is comprised of a collection of consolidated parameters for the key parts of the muon collider. These consolidated parameters follow on from the October 2024 Preliminary Parameters Report. Attention has been given to a high-level consistent set of baseline parameters throughout all systems of the complex, following a 10 TeV center-of-mass design. Additional details of the designs contributing to this baseline design are featured in the appendix. Likewise, explorative variations from this baseline set can be found in the appendix. The data is collected from a collaborative spreadsheet and transferred to overleaf. △ Less

Submitted 31 October, 2025; originally announced October 2025.

Surface Optimisation Governs the Local Design of Physical Networks

Authors: Xiangyi Meng, Benjamin Piazza, Csaba Both, Baruch Barzel, Albert-László Barabási

Abstract: The brain's connectome and the vascular system are examples of physical networks whose tangible nature influences their structure, layout, and ultimately their function. The material resources required to build and maintain these networks have inspired decades of research into wiring economy, offering testable predictions about their expected architecture and organisation. Here we empirically expl… ▽ More The brain's connectome and the vascular system are examples of physical networks whose tangible nature influences their structure, layout, and ultimately their function. The material resources required to build and maintain these networks have inspired decades of research into wiring economy, offering testable predictions about their expected architecture and organisation. Here we empirically explore the local branching geometry of a wide range of physical networks, uncovering systematic violations of the long-standing predictions of length and volume minimisation. This leads to the hypothesis that predicting the true material cost of physical networks requires us to account for their full three-dimensional geometry, resulting in a largely intractable optimisation problem. We discover, however, an exact mapping of surface minimisation onto high-dimensional Feynman diagrams in string theory, predicting that with increasing link thickness, a locally tree-like network undergoes a transition into configurations that can no longer be explained by length minimisation. Specifically, surface minimisation predicts the emergence of trifurcations and branching angles in excellent agreement with the local tree organisation of physical networks across a wide range of application domains. Finally, we predict the existence of stable orthogonal sprouts, which not only are prevalent in real networks but also play a key functional role, improving synapse formation in the brain and nutrient access in plants and fungi. △ Less

Submitted 27 September, 2025; originally announced September 2025.

Comments: 23 pages, 5 figures + Supplementary Information 48 pages

Self-supervised neural operator for solving partial differential equations

Authors: Wen You, Shaoqian Zhou, Xuhui Meng

Abstract: Neural operators (NOs) provide a new paradigm for efficiently solving partial differential equations (PDEs), but their training depends on costly high-fidelity data from numerical solvers, limiting applications in complex systems. We propose a self-supervised neural operator (SNO) that generates accurate and diverse training data on the fly without numerical solvers. SNO consists of three parts: a… ▽ More Neural operators (NOs) provide a new paradigm for efficiently solving partial differential equations (PDEs), but their training depends on costly high-fidelity data from numerical solvers, limiting applications in complex systems. We propose a self-supervised neural operator (SNO) that generates accurate and diverse training data on the fly without numerical solvers. SNO consists of three parts: a physics-informed sampler (PI-sampler) based on Bayesian PINNs for efficient data generation, a function encoder (FE) for compact input-output representations, and an encoder-only Transformer for operator learning, mapping boundary/initial conditions, source terms, and geometries to PDE solutions. We validate SNO on 1D steady/unsteady nonlinear reaction-diffusion equations, a 2D nonlinear PDE with varying geometries, and vortex-induced vibration of a flexible cylinder in fluid dynamics. SNO achieves high accuracy in all cases, and lightweight finetuning (O(100) trainable variables) further improves predictions with only a few hundred steps. This work provides a new route toward pretrained foundation models as efficient PDE surrogates. △ Less

Submitted 15 May, 2026; v1 submitted 31 August, 2025; originally announced September 2025.

1000-Channel Integrated Optical Phased Array with 180° Field of View, High Resolution and High Scalability

Authors: Yong Liu, Xiansong Meng, Hao Hu

Abstract: Optical phased array (OPA) is a promising technology for compact, solid-state beam steering, with applications ranging from free-space optical communication to LiDAR. However, simultaneously achieving a large field of view (FOV), high resolution, and low side-lobe level (SLL) remains a major challenge. Traditional OPAs face inherent limitations: they exhibit grating lobes when emitter spacing exce… ▽ More Optical phased array (OPA) is a promising technology for compact, solid-state beam steering, with applications ranging from free-space optical communication to LiDAR. However, simultaneously achieving a large field of view (FOV), high resolution, and low side-lobe level (SLL) remains a major challenge. Traditional OPAs face inherent limitations: they exhibit grating lobes when emitter spacing exceeds half the operating wavelength, while at half-wavelength spacing, significant crosstalk issues persist. Previously, we demonstrated a small-scale OPA that harnesses near-field interference and beamforming via a trapezoidal slab grating and a half-wavelength-pitch waveguide array to achieve a 180° FOV. However, its resolution was limited by the small channel count. In this work, we present a 1000-channel OPA that scales this architecture while addressing key challenges in waveguide crosstalk and control complexity. By optimizing waveguide routing, we minimize inter-channel coupling in the dense waveguide array. Additionally, we propose and demonstrate a passive matrix control scheme using 20 row and 50 column pulse-width modulation (PWM) signals to arbitrarily control 1000 thermo-optic phase shifters, significantly simplifying the electronic control and packaging. Our OPA achieves grating-lobe-free beam steering across a full 180° FOV, with a high resolution of 0.07° * 0.17° and a minimum SLL of -18.7 dB at 0°. This large-scale, cost-effective chip-based OPA paves the way for next-generation high-resolution, wide-angle beam steering systems. △ Less

Submitted 27 August, 2025; originally announced August 2025.

Comments: 17 pages

Observation and Control of Chiral Spin Frustration in BiYIG Thin Films

Authors: Jinlong Wang, Hanchen Wang, Zhewen Xu, Artim L. Bassant, Junfeng Hu, Wenjie Song, Chaozhong Li, Xiangrui Meng, Mengqi Zhao, Song Liu, Guozhi Chai, Peng Gao, Wanjun Jiang, Desheng Xue, Dapeng Yu, William Legrand, Christian L. Degen, Rembert A. Duine, Pietro Gambardella, Haiming Yu

Abstract: Chiral interactions within magnetic layers stabilize the formation of noncollinear spin textures, which can be leveraged to design devices with tailored magnetization dynamics. Here, we introduce chiral spin frustration in which energetically degenerate magnetic states frustrate the Dzyaloshinskii-Moriya interaction. We demonstrate magnon-driven switching of the chirally frustrated spin states in… ▽ More Chiral interactions within magnetic layers stabilize the formation of noncollinear spin textures, which can be leveraged to design devices with tailored magnetization dynamics. Here, we introduce chiral spin frustration in which energetically degenerate magnetic states frustrate the Dzyaloshinskii-Moriya interaction. We demonstrate magnon-driven switching of the chirally frustrated spin states in Bi-substituted yttrium iron garnet thin films. These states are defined by an in-plane macrospin neighboring two out-ofplane spins on either side with opposing chirality. Using scanning nitrogen-vacancy magnetometry and spin pumping, we identified four degenerate frustrated states and achieved their controllable switching via magnon spin torque. Crucially, the switching is unidirectional, with selectivity determined by the incoming magnon direction. This mechanism provides a powerful approach to manipulate frustrated spin states with magnons. Chiral spin frustration unlocks the geometry constraints of conventional frustration, and therefore opens new horizons for frustrated magnetism, paving the way for energy-efficient spintronic devices based on frustratio △ Less

Submitted 9 August, 2025; originally announced August 2025.

Comments: Accepted version, 8 pages, 5 figures

Journal ref: Physical Review Letters 135, 066705 (2025)

Eavesdropping Risk in Terahertz Channels by Covered Wavy Surfaces

Authors: Peian Li, Wenbo Liu, Jiabiao Zhao, Jiayuan Cui, Yapeng Ge, Qiang Niu, Yuping Yang, Xiangzhu Meng, Jianjun Ma

Abstract: Terahertz communications offer unprecedented data rates for next-generation wireless networks but suffer blockage susceptibility that restrict coverage and introduce physical-layer security vulnerabilities. Non-line-of-sight relay schemes using metallic wavy surfaces (MWS) address coverage limitations but require concealment beneath indoor materials for practical deployment. This work investigates… ▽ More Terahertz communications offer unprecedented data rates for next-generation wireless networks but suffer blockage susceptibility that restrict coverage and introduce physical-layer security vulnerabilities. Non-line-of-sight relay schemes using metallic wavy surfaces (MWS) address coverage limitations but require concealment beneath indoor materials for practical deployment. This work investigates THz channel characteristics and security vulnerabilities when MWS surfaces are covered with wallpaper, curtain, and wall plaster across 113-170 GHz. Results reveal that covering materials redistribute rather than eliminate eavesdropping threats, with persistent feasible interception scenarios remaining undetectable through conventional backscattering monitoring. These findings underscore the need for enhanced mechanisms designed for covered reflecting elements. △ Less

Submitted 6 August, 2025; originally announced August 2025.

Comments: Submitted to Optics Express

Ill posedness in shallow multi-phase debris flow models

Authors: Jake Langham, Xiannan Meng, Jamie P. Webb, Chris G. Johnson, J. M. N. T. Gray

Abstract: Depth-averaged systems of equations describing the motion of fluid-sediment mixtures have been widely adopted by scientists in pursuit of models that can predict the paths of dangerous overland flows of debris. As models have become increasingly sophisticated, many have been developed from a multi-phase perspective in which separate, but mutually coupled sets of equations govern the evolution of d… ▽ More Depth-averaged systems of equations describing the motion of fluid-sediment mixtures have been widely adopted by scientists in pursuit of models that can predict the paths of dangerous overland flows of debris. As models have become increasingly sophisticated, many have been developed from a multi-phase perspective in which separate, but mutually coupled sets of equations govern the evolution of different components of the mixture. However, this creates the opportunity for the existence of pathological instabilities stemming from resonant interactions between the phases. With reference to the most popular approaches, analyses of two- and three-phase models are performed, which demonstrate that they are more often than not ill posed as initial value problems over physically relevant parameter regimes - an issue which renders them unsuitable for scientific applications. Additionally, a general framework for detecting ill posedness in models with any number of phases is developed. This is used to show that small diffusive terms in the equations for momentum transport, which are sometimes neglected, can reliably eliminate this issue. Conditions are derived for the regularisation of models in this way, but they are typically not met by multi-phase models that feature diffusive terms. △ Less

Submitted 4 April, 2026; v1 submitted 27 May, 2025; originally announced May 2025.

Comments: 34 pages, 9 figures + 2 supplemental movies (see ancillary files)

Journal ref: Journal of Fluid Mechanics (2025), 1015, A52; Corrigendum: Journal of Fluid Mechanics (2025), 1022, E2

The Muon Collider

Authors: Carlotta Accettura, Simon Adrian, Rohit Agarwal, Claudia Ahdida, Chiara Aime', Avni Aksoy, Gian Luigi Alberghi, Siobhan Alden, Luca Alfonso, Muhammad Ali, Anna Rita Altamura, Nicola Amapane, Kathleen Amm, David Amorim, Paolo Andreetto, Fabio Anulli, Ludovica Aperio Bella, Rob Appleby, Artur Apresyan, Pouya Asadi, Mohammed Attia Mahmoud, Bernhard Auchmann, John Back, Anthony Badea, Kyu Jung Bae , et al. (433 additional authors not shown)

Abstract: Muons offer a unique opportunity to build a compact high-energy electroweak collider at the 10 TeV scale. A Muon Collider enables direct access to the underlying simplicity of the Standard Model and unparalleled reach beyond it. It will be a paradigm-shifting tool for particle physics representing the first collider to combine the high-energy reach of a proton collider and the high precision of an… ▽ More Muons offer a unique opportunity to build a compact high-energy electroweak collider at the 10 TeV scale. A Muon Collider enables direct access to the underlying simplicity of the Standard Model and unparalleled reach beyond it. It will be a paradigm-shifting tool for particle physics representing the first collider to combine the high-energy reach of a proton collider and the high precision of an electron-positron collider, yielding a physics potential significantly greater than the sum of its individual parts. A high-energy muon collider is the natural next step in the exploration of fundamental physics after the HL-LHC and a natural complement to a future low-energy Higgs factory. Such a facility would significantly broaden the scope of particle colliders, engaging the many frontiers of the high energy community. The last European Strategy for Particle Physics Update and later the Particle Physics Project Prioritisation Panel in the US requested a study of the muon collider, which is being carried on by the International Muon Collider Collaboration. In this comprehensive document we present the physics case, the state of the work on accelerator design and technology, and propose an R\&D project that can make the muon collider a reality. △ Less

Submitted 30 April, 2025; originally announced April 2025.

Comments: 406 pages, supplementary report to the European Strategy for Particle Physics - 2026 update

Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations

Authors: Shaoqian Zhou, Wen You, Ling Guo, Xuhui Meng

Abstract: Physics-informed deep learning approaches have been developed to solve forward and inverse stochastic differential equation (SDE) problems with high-dimensional stochastic space. However, the existing deep learning models have difficulties solving SDEs with high-dimensional spatial space. In the present study, we propose a scalable physics-informed deep generative model (sPI-GeM), which is capable… ▽ More Physics-informed deep learning approaches have been developed to solve forward and inverse stochastic differential equation (SDE) problems with high-dimensional stochastic space. However, the existing deep learning models have difficulties solving SDEs with high-dimensional spatial space. In the present study, we propose a scalable physics-informed deep generative model (sPI-GeM), which is capable of solving SDE problems with both high-dimensional stochastic and spatial space. The sPI-GeM consists of two deep learning models, i.e., (1) physics-informed basis networks (PI-BasisNet), which are used to learn the basis functions as well as the coefficients given data on a certain stochastic process or random field, and (2) physics-informed deep generative model (PI-GeM), which learns the distribution over the coefficients obtained from the PI-BasisNet. The new samples for the learned stochastic process can then be obtained using the inner product between the output of the generator and the basis functions from the trained PI-BasisNet. The sPI-GeM addresses the scalability in the spatial space in a similar way as in the widely used dimensionality reduction technique, i.e., principal component analysis (PCA). A series of numerical experiments, including approximation of Gaussian and non-Gaussian stochastic processes, forward and inverse SDE problems, are performed to demonstrate the accuracy of the proposed model. Furthermore, we also show the scalability of the sPI-GeM in both the stochastic and spatial space using an example of a forward SDE problem with 38- and 20-dimension stochastic and spatial space, respectively. △ Less

Submitted 4 March, 2026; v1 submitted 23 March, 2025; originally announced March 2025.

Highly Uniform Thermally Undercut Silicon Photonic Devices in a 300 mm CMOS Foundry Process

Authors: Robert Parsons, Kaylx Jang, Yuyang Wang, Asher Novick, A. Matthew Smith, Christopher C. Tison, Yonas Gebregiorgis, Venkatesh Deenadayalan, Matthew van Niekerk, Lewis Carpenter, Tat Ngai, Gerald Leake, Daniel Coleman, Xiang Meng, Stefan Preble, Michael L. Fanto, Keren Bergman, Anthony Rizzo

Abstract: Silicon photonic devices fundamental to high-density wavelength-division multiplexed (DWDM) optical links and photonic switching networks, such as resonant modulators and Mach-Zehnder interferometers (MZIs), are highly sensitive to fabrication variations and operational temperature swings. However, thermal tuning to compensate for fabrication and operational temperature variations can result in pr… ▽ More Silicon photonic devices fundamental to high-density wavelength-division multiplexed (DWDM) optical links and photonic switching networks, such as resonant modulators and Mach-Zehnder interferometers (MZIs), are highly sensitive to fabrication variations and operational temperature swings. However, thermal tuning to compensate for fabrication and operational temperature variations can result in prohibitive power consumption, challenging the scalability of energy-efficient photonic integrated circuits (PICs). In this work, we develop and demonstrate a wafer-scale thermal undercut process in a 300 mm complementary metal oxide semiconductor (CMOS) foundry that dramatically improves the thermal isolation of thermo-optic devices by selectively removing substrate material beneath the waveguides and resonators. This approach significantly reduces the power required for thermal tuning across multiple device architectures, achieving almost a 5$\times$ improvement in tuning efficiency in a state-of-the-art 4.5 $μ$m radius microdisk modulator and a 40$\times$ improvement in efficiency for a MZI phase shifter. To the best of the authors' knowledge, we demonstrate the first wafer-scale comparison of non-undercut and undercut silicon photonic devices using comprehensive wafer-scale measurements across 64 reticles of a 300 mm silicon-on-insulator (SOI) wafer. Further, we demonstrate a comprehensive wafer-scale analysis of the influence of undercut trench opening geometry on device tuning efficiency. Notably, we observe highly uniform performance across the full 300 mm wafer for multiple device types, emphasizing that our process can be scaled to large-scale photonic circuits with high yield. These results open new opportunities for large-scale integrated photonic circuits using thermo-optic devices, paving the way for scalable, low-power silicon photonic systems. △ Less

Submitted 6 June, 2025; v1 submitted 11 March, 2025; originally announced March 2025.

Comments: 11 pages, 6 figures

Experimental Demonstration of Logical Magic State Distillation

Authors: Pedro Sales Rodriguez, John M. Robinson, Paul Niklas Jepsen, Zhiyang He, Casey Duckering, Chen Zhao, Kai-Hsin Wu, Joseph Campo, Kevin Bagnall, Minho Kwon, Thomas Karolyshyn, Phillip Weinberg, Madelyn Cain, Simon J. Evered, Alexandra A. Geim, Marcin Kalinowski, Sophie H. Li, Tom Manovitz, Jesse Amato-Grill, James I. Basham, Liane Bernstein, Boris Braverman, Alexei Bylinskii, Adam Choukri, Robert DeAngelo , et al. (48 additional authors not shown)

Abstract: Realizing universal fault-tolerant quantum computation is a key goal in quantum information science. By encoding quantum information into logical qubits utilizing quantum error correcting codes, physical errors can be detected and corrected, enabling substantial reduction in logical error rates. However, the set of logical operations that can be easily implemented on such encoded qubits is often c… ▽ More Realizing universal fault-tolerant quantum computation is a key goal in quantum information science. By encoding quantum information into logical qubits utilizing quantum error correcting codes, physical errors can be detected and corrected, enabling substantial reduction in logical error rates. However, the set of logical operations that can be easily implemented on such encoded qubits is often constrained, necessitating the use of special resource states known as 'magic states' to implement universal, classically hard circuits. A key method to prepare high-fidelity magic states is to perform 'distillation', creating them from multiple lower fidelity inputs. Here we present the experimental realization of magic state distillation with logical qubits on a neutral-atom quantum computer. Our approach makes use of a dynamically reconfigurable architecture to encode and perform quantum operations on many logical qubits in parallel. We demonstrate the distillation of magic states encoded in d=3 and d=5 color codes, observing improvements of the logical fidelity of the output magic states compared to the input logical magic states. These experiments demonstrate a key building block of universal fault-tolerant quantum computation, and represent an important step towards large-scale logical quantum processors. △ Less

Submitted 19 December, 2024; originally announced December 2024.

Comments: 8+11 pages, 4+4 figures

Journal ref: Nature 645, 620-625 (2025)

MuCol Milestone Report No. 5: Preliminary Parameters

Authors: Carlotta Accettura, Simon Adrian, Rohit Agarwal, Claudia Ahdida, Chiara Aimé, Avni Aksoy, Gian Luigi Alberghi, Siobhan Alden, Luca Alfonso, Nicola Amapane, David Amorim, Paolo Andreetto, Fabio Anulli, Rob Appleby, Artur Apresyan, Pouya Asadi, Mohammed Attia Mahmoud, Bernhard Auchmann, John Back, Anthony Badea, Kyu Jung Bae, E. J. Bahng, Lorenzo Balconi, Fabrice Balli, Laura Bandiera , et al. (369 additional authors not shown)

Abstract: This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power… ▽ More This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf. △ Less

Submitted 5 November, 2024; originally announced November 2024.

NeDF: neural deflection fields for sparse-view tomographic background oriented Schlieren

Authors: Jiawei Li, Xuhui Meng, Yuan Xiong, Tong Jia, Chong Pan, Jinjun Wang

Abstract: Three-dimensional (3D) density-varying turbulent flows are widely encountered in high-speed aerodynamics, combustion, and heterogeneous mixing processes. Multi-camera-based tomographic background-oriented Schlieren (TBOS) has emerged as a powerful technique for revealing 3D flow density structures. However, dozens of cameras are typically required to obtain high-quality reconstructed density field… ▽ More Three-dimensional (3D) density-varying turbulent flows are widely encountered in high-speed aerodynamics, combustion, and heterogeneous mixing processes. Multi-camera-based tomographic background-oriented Schlieren (TBOS) has emerged as a powerful technique for revealing 3D flow density structures. However, dozens of cameras are typically required to obtain high-quality reconstructed density fields. Limited by the number of available optical windows and confined space in the harsh experimental environments, TBOS with only sparse views and limited viewing angles often becomes the necessary choice practically, rendering the inverse problem for TBOS reconstruction severely ill-posed and resulting in degraded tomography quality. In this study, we propose a novel TBOS reconstruction method, neural deflection field (NeDF), utilizing deep neural networks (DNNs) to represent the density gradient fields without using any pretrained neural network models. Particularly, state-of-the-art positional encoding techniques and hierarchical sampling strategies are incorporated to capture the density structures of high spatial frequencies. Required background images for TBOS reconstructions are synthesized based on a high-fidelity nonlinear ray-tracing method with the ground truth flows from conducting LES simulations on premixed turbulent flames. Owing to these synthesized BOS images, the superiority of the proposed method is quantitatively verified compared to the classical TBOS reconstruction methods, and the specific contributions from the position encoding and the hierarchical sampling strategy are also elucidated. △ Less

Submitted 30 September, 2024; originally announced September 2024.

Comments: 17 pages, 4 figures, 1 table (paper); 3 pages, 2 figures, 3 tables (supplementary material)

Monte Carlo Physics-informed neural networks for multiscale heat conduction via phonon Boltzmann transport equation

Authors: Qingyi Lin, Chuang Zhang, Xuhui Meng, Zhaoli Guo

Abstract: The phonon Boltzmann transport equation (BTE) is widely used for describing multiscale heat conduction (from nm to $μ$m or mm) in solid materials. Developing numerical approaches to solve this equation is challenging since it is a 7-dimensional integral-differential equation. In this work, we propose Monte Carlo physics-informed neural networks (MC-PINNs), which do not suffer from the "curse of di… ▽ More The phonon Boltzmann transport equation (BTE) is widely used for describing multiscale heat conduction (from nm to $μ$m or mm) in solid materials. Developing numerical approaches to solve this equation is challenging since it is a 7-dimensional integral-differential equation. In this work, we propose Monte Carlo physics-informed neural networks (MC-PINNs), which do not suffer from the "curse of dimensionality", to solve the phonon BTE to model the multiscale heat conduction in solid materials. MC-PINNs use a deep neural network to approximate the solution to the BTE, and encode the BTE as well as the corresponding boundary/initial conditions using the automatic differentiation. In addition, we propose a novel two-step sampling approach to address inefficiency and inaccuracy issues in the widely used sampling methods in PINNs. In particular, we first randomly sample a certain number of points in the temporal-spatial space (Step I), and then draw another number of points randomly in the solid angular space (Step II). The training points at each step are constructed based on the data drawn from the above two steps using the tensor product. The two-step sampling strategy enables MC-PINNs (1) to model the heat conduction from ballistic to diffusive regimes, and (2) is more memory-efficient compared to conventional numerical solvers or existing PINNs for BTE. A series of numerical examples including quasi-one-dimensional (quasi-1D) steady/unsteady heat conduction in a film, and the heat conduction in a quasi-two- and three-dimensional square domains, are conducted to justify the effectiveness of the MC-PINNs for heat conduction spanning diffusive and ballistic regimes. Finally, we compare the computational time and memory usage of the MC-PINNs and one of the state-of-the-art numerical methods to demonstrate the potential of the MC-PINNs for large scale problems in real-world applications. △ Less

Submitted 28 October, 2024; v1 submitted 20 August, 2024; originally announced August 2024.

Metal-Organic Framework-Derived Sugarcoated Haws-like AgNWs/ZIF-8/Pd for Plasmon-Promoted Photocatalytic Hydrogenation

Authors: Shuoren Li, Xingxing Meng, Leilei Diao, Jing Wang, Chuanping Li

Abstract: The conversion of biomass-derived materials into value-added products via photocatalysis holds significant promise in driving the development of renewable resources. However, since catalytic processes often require high temperatures and pressures, most catalysts are quite difficult to meet the requirements of high selectivity and high activity simultaneously. Herein, a plasmon-promoted photocataly… ▽ More The conversion of biomass-derived materials into value-added products via photocatalysis holds significant promise in driving the development of renewable resources. However, since catalytic processes often require high temperatures and pressures, most catalysts are quite difficult to meet the requirements of high selectivity and high activity simultaneously. Herein, a plasmon-promoted photocatalyst, integrating Ag nanowires with metal-organic frameworks (MOFs)-confined Pd nanoparticles, is rationally designed to afford AgNWs/ZIF-8/Pd which achieve highly selective and efficient catalytic hydrogenation toward 2(5H)-Furanone under mild conditions. The plasmonic AgNWs/ZIF-8/Pd exhibited much stronger photocatalytic activity and higher selectivity compared with AgNWS/ZIF-8 and ZIF-8/Pd. The enhanced activity can be attributed to the synergistic coupling between Pd nanoparticles and AgNWs, and the possible reaction mechanism is proposed. This work provides a new approach to constructing efficient photocatalysts and offers new insights into the understanding of the influence of the plasmonic effect on photocatalytic hydrogenation reactions. △ Less

Submitted 5 August, 2024; originally announced August 2024.

Interim report for the International Muon Collider Collaboration (IMCC)

Authors: C. Accettura, S. Adrian, R. Agarwal, C. Ahdida, C. Aimé, A. Aksoy, G. L. Alberghi, S. Alden, N. Amapane, D. Amorim, P. Andreetto, F. Anulli, R. Appleby, A. Apresyan, P. Asadi, M. Attia Mahmoud, B. Auchmann, J. Back, A. Badea, K. J. Bae, E. J. Bahng, L. Balconi, F. Balli, L. Bandiera, C. Barbagallo , et al. (362 additional authors not shown)

Abstract: The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accele… ▽ More The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider. △ Less

Submitted 28 January, 2025; v1 submitted 17 July, 2024; originally announced July 2024.

Comments: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

Study of the decay and production properties of $D_{s1}(2536)$ and $D_{s2}^*(2573)$

Authors: M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (645 additional authors not shown)

Abstract: The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be… ▽ More The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be $(35.9\pm 4.8\pm 3.5)\%$ and $(37.4\pm 3.1\pm 4.6)\%$, respectively. The measurements are in tension with predictions based on the assumption that the $D_{s1}(2536)$ and $D_{s2}^*(2573)$ are dominated by a bare $c\bar{s}$ component. The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ cross sections are measured, and a resonant structure at around 4.6~GeV with a width of 50~MeV is observed for the first time with a statistical significance of $15σ$ in the $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ process. It could be the $Y(4626)$ found by the Belle collaboration in the $D_s^+D_{s1}(2536)^{-}$ final state, since they have similar masses and widths. There is also evidence for a structure at around 4.75~GeV in both processes. △ Less

Submitted 10 July, 2024; originally announced July 2024.

A Broadband Algorithm for Adiabatic Mode Evolution and its Application on Polarization Splitter-Rotator on LNOI Platform

Authors: Geng Chen, Chijun Li, Xuanhao Wang, An Pan, Junjie Wei, Yuankang Huang, Siyu Lu, Yiqi Dai, Xiangyu Meng, Cheng Zeng, Jinsong Xia

Abstract: Adiabatic mode evolution waveguides (AMEWs) are widely utilized in integrated photonics, including tapered waveguides, edge couplers, mode converters, splitters, etc. An analytical theory and a novel AMEW design algorithm are developed to create shortcuts to adiabaticity (STA). This new algorithm is effective in shortening the total length of the AMEW while maintaining the desired wavelength range… ▽ More Adiabatic mode evolution waveguides (AMEWs) are widely utilized in integrated photonics, including tapered waveguides, edge couplers, mode converters, splitters, etc. An analytical theory and a novel AMEW design algorithm are developed to create shortcuts to adiabaticity (STA). This new algorithm is effective in shortening the total length of the AMEW while maintaining the desired wavelength range. Moreover, this analytical algorithm requires much fewer computing resources than traditional numerical algorithms. With the new algorithm, we demonstrate a broadband and highly efficient polarization splitter-rotator (PSR) on a lithium-niobate-on-insulator (LNOI) platform with an LN thickness of 500 nm. According to our simulation, the length of the PSR is shortened by 3.5 times compared to the linear design. The fabricated PSR, with a total length of 2 mm, exhibits an insertion loss (IL) of 0.8 dB and a polarization extinction ratio (ER) of 12.2 dB over a wavelength range exceeding 76 nm. △ Less

Submitted 22 July, 2024; v1 submitted 6 July, 2024; originally announced July 2024.

Comments: 16 pages, 6 figures, 2 tables

A Unified Intracellular pH Landscape with SITE-pHorin: a Quantum-Entanglement-Enhanced pH Probe

Authors: Shu-Ang Li, Xiao-Yan Meng, Su Zhang, Ying-Jie Zhang, Run-Zhou Yang, Dian-Dian Wang, Yang Yang, Pei-Pei Liu, Jian-Sheng Kang

Abstract: An accurate map of intracellular organelle pH is crucial for comprehending cellular metabolism and organellar functions. However, a unified intracellular pH spectrum using a single probe is still lack. Here, we developed a novel quantum entanglement-enhanced pH-sensitive probe called SITE-pHorin, which featured a wide pH-sensitive range and ratiometric quantitative measurement capabilities. Subseq… ▽ More An accurate map of intracellular organelle pH is crucial for comprehending cellular metabolism and organellar functions. However, a unified intracellular pH spectrum using a single probe is still lack. Here, we developed a novel quantum entanglement-enhanced pH-sensitive probe called SITE-pHorin, which featured a wide pH-sensitive range and ratiometric quantitative measurement capabilities. Subsequently, we measured the pH of various organelles and their sub-compartments, including mitochondrial sub-spaces, Golgi stacks, endoplasmic reticulum, lysosomes, peroxisomes, and endosomes in COS-7 cells. For the long-standing debate on mitochondrial compartments pH, we measured the pH of mitochondrial cristae as 6.60 \pm 0.40, the pH of mitochondrial intermembrane space as 6.95 \pm 0.30, and two populations of mitochondrial matrix pH at approximately 7.20 \pm 0.27 and 7.50 \pm 0.16, respectively. Notably, the lysosome pH exhibited a single, narrow Gaussian distribution centered at 4.79 \pm 0.17. Furthermore, quantum chemistry computations revealed that both the deprotonation of the residue Y182 and the discrete curvature of deformed benzene ring in chromophore are both necessary for the quantum entanglement mechanism of SITE-pHorin. Intriguingly, our findings reveal an accurate pH gradient (0.6-0.9 pH unit) between mitochondrial cristae and matrix, suggesting prior knowledge about ΔpH (0.4-0.6) and mitochondrial proton motive force (pmf) are underestimated. △ Less

Submitted 4 July, 2024; originally announced July 2024.

Comments: 64 pages, 7 figures, the supplemental material contains 13 supplemental figures and 4 supplemental tables

Large-scale quantum reservoir learning with an analog quantum computer

Authors: Milan Kornjača, Hong-Ye Hu, Chen Zhao, Jonathan Wurtz, Phillip Weinberg, Majd Hamdan, Andrii Zhdanov, Sergio H. Cantu, Hengyun Zhou, Rodrigo Araiza Bravo, Kevin Bagnall, James I. Basham, Joseph Campo, Adam Choukri, Robert DeAngelo, Paige Frederick, David Haines, Julian Hammett, Ning Hsu, Ming-Guang Hu, Florian Huber, Paul Niklas Jepsen, Ningyuan Jia, Thomas Karolyshyn, Minho Kwon , et al. (28 additional authors not shown)

Abstract: Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lac… ▽ More Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks, including binary and multi-class classification, as well as timeseries prediction. Effective and improving learning is observed with increasing system sizes of up to 108 qubits, demonstrating the largest quantum machine learning experiment to date. We further observe comparative quantum kernel advantage in learning tasks by constructing synthetic datasets based on the geometric differences between generated quantum and classical data kernels. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning. We expect these results to stimulate further extensions to different quantum hardware and machine learning paradigms, including early fault-tolerant hardware and generative machine learning tasks. △ Less

Submitted 2 July, 2024; originally announced July 2024.

Comments: 10 + 14 pages, 4 + 7 figures

Multipartite Entanglement Routing as a Hypergraph Immersion Problem

Authors: Yu Tian, Yuefei Liu, Xiangyi Meng

Abstract: Multipartite entanglement, linking multiple nodes simultaneously, is a higher-order correlation that offers advantages over pairwise connections in quantum networks (QNs). Creating reliable, large-scale multipartite entanglement requires entanglement routing, a process that combines local, short-distance connections into a long-distance connection, which can be considered as a transformation of ne… ▽ More Multipartite entanglement, linking multiple nodes simultaneously, is a higher-order correlation that offers advantages over pairwise connections in quantum networks (QNs). Creating reliable, large-scale multipartite entanglement requires entanglement routing, a process that combines local, short-distance connections into a long-distance connection, which can be considered as a transformation of network topology. Here, we address the question of whether a QN can be topologically transformed into another via entanglement routing. Our key result is an exact mapping from multipartite entanglement routing to Nash-Williams's graph immersion problem, extended to hypergraphs. This generalized hypergraph immersion problem introduces a partial order between QN topologies, permitting certain topological transformations while precluding others, offering discerning insights into the design and manipulation of higher-order network topologies in QNs. △ Less

Submitted 19 December, 2024; v1 submitted 19 June, 2024; originally announced June 2024.

Comments: 11 pages, 6 figures, 1 table

High Discrimination Ratio, Broadband Circularly Polarized Light Photodetector Using Dielectric Achiral Nanostructures

Authors: Guanyu Zhang, Xiaying Lyu, Yulu Qin, Yaolong Li, Zipu Fan, Xianghan Meng, Yuqing Cheng, Zini Cao, Yixuan Xu, Dong Sun, Yunan Gao, Qihuang Gong, Guowei Lu

Abstract: The on-chip measurement of polarization states plays an increasingly crucial role in modern sensing and imaging applications. While high-performance monolithic linearly polarized photodetectors have been extensively studied, integrated circularly polarized light (CPL) photodetectors are still hindered by inadequate discrimination capability. In this study, we employ achiral all-dielectric nanostru… ▽ More The on-chip measurement of polarization states plays an increasingly crucial role in modern sensing and imaging applications. While high-performance monolithic linearly polarized photodetectors have been extensively studied, integrated circularly polarized light (CPL) photodetectors are still hindered by inadequate discrimination capability. In this study, we employ achiral all-dielectric nanostructures to develop a broadband CPL photodetector with an impressive discrimination ratio of ~107 at the wavelength of 405 nm, significantly surpassing its counterparts by two orders of magnitude. Our device shows outstanding CPL discrimination capability across the visible band without requiring intensity calibration. Its function mechanism is based on the CPL-dependent near-field modes within achiral structures: under left or right CPL illumination, distinct near-field modes are excited, resulting in asymmetric irradiation of the two electrodes and generating a photovoltage with directions determined by the chirality of the incident light field. The proposed design strategy facilitates the realization of ultra-compact CPL detection across diverse materials, structures, and spectral ranges, presenting a novel avenue for achieving high-performance monolithic CPL detection. △ Less

Submitted 19 May, 2024; originally announced May 2024.

Comments: 20 pages, 4 figures

Journal ref: Light Sci Appl 13, 275 (2024)

The effect of collision-coagulation on the mean relative velocity of particles in turbulent flow: systematic results and validation of model

Authors: Xiaohui Meng, Ewe-Wei Saw

Abstract: The mean radial component of relative velocity (MRV) between pairs of inertial particles is studied, where the particles are advected by turbulent flow and undergo collision-and-coagulation. A previously proposed phenomenological model of MRV for low-inertia particles \citep{saw2022intricate} is corrected (improved) and shown to produce better predictions of the MRV as a function of particle separ… ▽ More The mean radial component of relative velocity (MRV) between pairs of inertial particles is studied, where the particles are advected by turbulent flow and undergo collision-and-coagulation. A previously proposed phenomenological model of MRV for low-inertia particles \citep{saw2022intricate} is corrected (improved) and shown to produce better predictions of the MRV as a function of particle separation distance $r$. Using direct numerical simulation (DNS), the relationship between the MRV and particle/turbulent parameters is studied. For particles with near-zero Stokes numbers ($St$), the MRV is roughly independent of $St$. At larger $St$, the magnitude of MRV increases with $St$, particularly when $St>0.2$. Assuming that the relative particle velocities are derived from fluid velocity differences associated with a nominal resonant length scale, an empirical relation between $St$ is obtained: $d+αSt^β$, where $β\approx1.86$. Coupled with this empirical result, the aforementioned MRV model could be extended to predict MRV for any finite $St$, and we show that the predictions are accurate against the DNS results. Our results also suggest that the extended model could also accurately account for possible Reynolds number ($Re_λ$) effect by simply allowing $α$ and $β$ to be functions of $Re_λ$. Additionally, when the particle diameter is smaller than the Kolmogorov length scale, the MRV for particles with the same St is independent of the particle diameter. The analysis under different Reynolds numbers ($Re_λ=84,124,189$) reveals that for particles with $St\ll1$, the MRV is $Re_λ$-independent. For larger $St$, $Re_λ$ dependence is observed such that the coefficients $α$ and $β$ decrease with $Re_λ. △ Less

Submitted 21 March, 2024; originally announced March 2024.

Comments: 12 pages, 1 table, 7 figures

Spatial Distribution of Inertial Particles in Turbulent Taylor-Couette Flow

Authors: Hao Jiang, Zhi-ming Lu, Bo-fu Wang, Xiao-hui Meng, Jie Shen, Kai Leong Chong

Abstract: This study investigates the spatial distribution of inertial particles in turbulent Taylor-Couette flow. Direct numerical simulations are performed using a one-way coupled Eulerian-Lagrangian approach, with a fixed inner wall Reynolds number of 2500 for the carrier flow, while the particle Stokes number varies from 0.034 to 1 for the dispersed phase. We first examine the issue of preferential conc… ▽ More This study investigates the spatial distribution of inertial particles in turbulent Taylor-Couette flow. Direct numerical simulations are performed using a one-way coupled Eulerian-Lagrangian approach, with a fixed inner wall Reynolds number of 2500 for the carrier flow, while the particle Stokes number varies from 0.034 to 1 for the dispersed phase. We first examine the issue of preferential concentration of particles near the outer wall region. Employing two-dimensional (2D) Voronoi analysis, we observe a pronounced particle clustering with increasing $St$, particularly evident in regions of low fluid velocity. Additionally, we investigate the concentration balance equation, inspired by the work of johnson et al.(2020), to examine particle radial distribution. We discern the predominant sources of influence, namely biased sampling, turbophoresis, and centrifugal effects. Across all cases, centrifugal force emerges as the primary driver, causing particle migration towards the outer wall. Biased sampling predominantly affects smaller inertial particles, driving them towards the inner wall due to sampling within Taylor rolls with inward radial velocity. Conversely, turbophoresis primarily impacts larger inertial particles, inducing migration towards both walls where turbulent intensity is weaker compared to the bulk. With the revealed physics, our work provides a basis for predicting and controlling particle movement and distribution in industrial applications. △ Less

Submitted 26 February, 2024; originally announced February 2024.

Comments: 18 pages, 11 figures

Digital-analog hybrid matrix multiplication processor for optical neural networks

Authors: Xiansong Meng, Deming Kong, Kwangwoong Kim, Qiuchi Li, Po Dong, Ingemar J. Cox, Christina Lioma, Hao Hu

Abstract: The computational demands of modern AI have spurred interest in optical neural networks (ONNs) which offer the potential benefits of increased speed and lower power consumption. However, current ONNs face various challenges,most significantly a limited calculation precision (typically around 4 bits) and the requirement for high-resolution signal format converters (digital-to-analogue conversions (… ▽ More The computational demands of modern AI have spurred interest in optical neural networks (ONNs) which offer the potential benefits of increased speed and lower power consumption. However, current ONNs face various challenges,most significantly a limited calculation precision (typically around 4 bits) and the requirement for high-resolution signal format converters (digital-to-analogue conversions (DACs) and analogue-to-digital conversions (ADCs)). These challenges are inherent to their analog computing nature and pose significant obstacles in practical implementation. Here, we propose a digital-analog hybrid optical computing architecture for ONNs, which utilizes digital optical inputs in the form of binary words. By introducing the logic levels and decisions based on thresholding, the calculation precision can be significantly enhanced. The DACs for input data can be removed and the resolution of the ADCs can be greatly reduced. This can increase the operating speed at a high calculation precision and facilitate the compatibility with microelectronics. To validate our approach, we have fabricated a proof-of-concept photonic chip and built up a hybrid optical processor (HOP) system for neural network applications. We have demonstrated an unprecedented 16-bit calculation precision for high-definition image processing, with a pixel error rate (PER) as low as $1.8\times10^{-3}$ at an signal-to-noise ratio (SNR) of 18.2 dB. We have also implemented a convolutional neural network for handwritten digit recognition that shows the same accuracy as the one achieved by a desktop computer. The concept of the digital-analog hybrid optical computing architecture offers a methodology that could potentially be applied to various ONN implementations and may intrigue new research into efficient and accurate domain-specific optical computing architectures for neural networks. △ Less

Submitted 26 January, 2024; originally announced January 2024.

Study on electromagnetically induced transparency effects in Dirac and VO$_2$ hybrid material structure

Authors: Di Ke, Xie Meng, Xia Hua Rong, Cheng An Yu, Liu Yu, Du Jia Jia

Abstract: In this paper, we present a metamaterial structure of Dirac and vanadium dioxide and investigate its optical properties using the finite-difference time-domain (FDTD) technique. Using the phase transition feature of vanadium dioxide, the design can realize active tuning of the PIT effect at terahertz frequency, thereby converting from a single PIT to a double PIT. When VO$_2$ is in the insulating… ▽ More In this paper, we present a metamaterial structure of Dirac and vanadium dioxide and investigate its optical properties using the finite-difference time-domain (FDTD) technique. Using the phase transition feature of vanadium dioxide, the design can realize active tuning of the PIT effect at terahertz frequency, thereby converting from a single PIT to a double PIT. When VO$_2$ is in the insulating state, the structure is symmetric to obtain a single-band PIT effect; When VO$_2$ is in the metallic state, the structure turns asymmetric to realize a dual-band PIT effect. This design provides a reference direction for the design of actively tunable metamaterials. Additionally, it is discovered that the transparent window's resonant frequency and the Dirac material's Fermi level in this structure have a somewhat linear relationship. In addition, the structure achieves superior refractive index sensitivity in the terahertz band, surpassing 1 THz/RIU. Consequently, the concept exhibits encouraging potential for application in refractive index sensors and optical switches. △ Less

Submitted 18 December, 2023; originally announced December 2023.

Adapting to climate change: Long-term impact of wind resource changes on China's power system resilience

Authors: Jiaqi Ruan, Xiangrui Meng, Yifan Zhu, Gaoqi Liang, Xianzhuo Sun, Huayi Wu, Huijuan Xiao, Mengqian Lu, Pin Gao, Jiapeng Li, Wai-Kin Wong, Zhao Xu, Junhua Zhao

Abstract: Modern society's reliance on power systems is at risk from the escalating effects of wind-related climate change. Yet, failure to identify the intricate relationship between wind-related climate risks and power systems could lead to serious short- and long-term issues, including partial or complete blackouts. Here, we develop a comprehensive framework to assess China's power system resilience acro… ▽ More Modern society's reliance on power systems is at risk from the escalating effects of wind-related climate change. Yet, failure to identify the intricate relationship between wind-related climate risks and power systems could lead to serious short- and long-term issues, including partial or complete blackouts. Here, we develop a comprehensive framework to assess China's power system resilience across various climate change scenarios, enabling a holistic evaluation of the repercussions induced by wind-related climate change. Our findings indicate that China's current wind projects and planning strategies could be jeopardized by wind-related climate change, with up to a 12\% decline in regional wind power availability. Moreover, our results underscore a pronounced vulnerability of power system resilience amidst the rigors of hastened climate change, unveiling a potential amplification of resilience deterioration, even approaching fourfold by 2060 under the most severe scenario, relative to the 2020 benchmark. This work advocates for strategic financial deployment within the power sector aimed at climate adaptation, enhancing power system resilience to avert profound losses from long-term, wind-influenced climatic fluctuations. △ Less

Submitted 24 January, 2024; v1 submitted 28 November, 2023; originally announced November 2023.

Comments: Not suitable for publication

Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators

Authors: Zongren Zou, Xuhui Meng, George Em Karniadakis

Abstract: Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly critical as neural networks (NNs) are being widely adopted in addressing complex problems across various scientific disciplines. Representative SciML models are physics-informed neural networks (PINNs) and neural operators (NOs). While UQ in SciML has been increasingly investigated in recent years, very few… ▽ More Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly critical as neural networks (NNs) are being widely adopted in addressing complex problems across various scientific disciplines. Representative SciML models are physics-informed neural networks (PINNs) and neural operators (NOs). While UQ in SciML has been increasingly investigated in recent years, very few works have focused on addressing the uncertainty caused by the noisy inputs, such as spatial-temporal coordinates in PINNs and input functions in NOs. The presence of noise in the inputs of the models can pose significantly more challenges compared to noise in the outputs of the models, primarily due to the inherent nonlinearity of most SciML algorithms. As a result, UQ for noisy inputs becomes a crucial factor for reliable and trustworthy deployment of these models in applications involving physical knowledge. To this end, we introduce a Bayesian approach to quantify uncertainty arising from noisy inputs-outputs in PINNs and NOs. We show that this approach can be seamlessly integrated into PINNs and NOs, when they are employed to encode the physical information. PINNs incorporate physics by including physics-informed terms via automatic differentiation, either in the loss function or the likelihood, and often take as input the spatial-temporal coordinate. Therefore, the present method equips PINNs with the capability to address problems where the observed coordinate is subject to noise. On the other hand, pretrained NOs are also commonly employed as equation-free surrogates in solving differential equations and Bayesian inverse problems, in which they take functions as inputs. The proposed approach enables them to handle noisy measurements for both input and output functions with UQ. △ Less

Submitted 19 November, 2023; originally announced November 2023.

High Performance Thin-film Lithium Niobate Modulator Applied ITO Composite Electrode with Modulation Efficiency of 1V*cm

Authors: Xiangyu Meng, Can Yuan, Xingran Cheng, Shuai Yuan, Chenglin Shang, An Pan, Zhicheng Qu, Xuanhao Wang, Peijie Zhang, Chengcheng Gui, Chao Chen, Cheng Zeng, Jinsong Xia

Abstract: Thin film lithium niobate (TFLN) based electro-optic modulator is widely applied in the field of broadband optical communications due to its advantages such as large bandwidth, high extinction ratio, and low optical loss, bringing new possibilities for the next generation of high-performance electro-optic modulators. However, the modulation efficiency of TFLN modulators is still relatively low whe… ▽ More Thin film lithium niobate (TFLN) based electro-optic modulator is widely applied in the field of broadband optical communications due to its advantages such as large bandwidth, high extinction ratio, and low optical loss, bringing new possibilities for the next generation of high-performance electro-optic modulators. However, the modulation efficiency of TFLN modulators is still relatively low when compared with Silicon and Indium-Phosphide (InP) based competitors. Due to the restriction of the trade-off between half-wave voltage and modulation length, it is difficult to simultaneously obtain low driving voltage and large modulating bandwidth. Here, we break this limitation by introducing Transparent Conductive Oxide (TCO) film, resulting in an ultra-high modulation efficiency of 1.02 V*cm in O-Band. The fabricated composite electrode not only achieves high modulation efficiency but also maintains a high electro-optic bandwidth, as demonstrated by the 3 dB roll-off at 108 GHz and the transmission of PAM-4 signals at 224 Gbit/s. Our device presents new solutions for the next generation of low-cost high-performance electro-optic modulators. Additionally, it paves the way for downsizing TFLN-based multi-channel optical transmitter chips. △ Less

Submitted 8 November, 2023; originally announced November 2023.

Comments: 16 pages, 8 figures

The nature and nurture of network evolution

Authors: Bin Zhou, Petter Holme, Zaiwu Gong, Choujun Zhan, Yao Huang, Xin Lu, Xiangyi Meng

Abstract: Although the origin of the fat-tail characteristic of the degree distribution in complex networks has been extensively researched, the underlying cause of the degree distribution characteristic across the complete range of degrees remains obscure. Here, we propose an evolution model that incorporates only two factors: the node's weight, reflecting its innate attractiveness (nature), and the node's… ▽ More Although the origin of the fat-tail characteristic of the degree distribution in complex networks has been extensively researched, the underlying cause of the degree distribution characteristic across the complete range of degrees remains obscure. Here, we propose an evolution model that incorporates only two factors: the node's weight, reflecting its innate attractiveness (nature), and the node's degree, reflecting the external influences (nurture). The proposed model provides a good fit for degree distributions and degree ratio distributions of numerous real-world networks and reproduces their evolution processes. Our results indicate that the nurture factor plays a dominant role in the evolution of social networks. In contrast, the nature factor plays a dominant role in the evolution of non-social networks, suggesting that whether nodes are people determines the dominant factor influencing the evolution of real-world networks. △ Less

Submitted 5 November, 2023; originally announced November 2023.

Comments: 8pages, 4 figures

Report number: 14

Journal ref: Nature Communications, 14, 7031, 2023

Percolation Theories for Quantum Networks

Authors: Xiangyi Meng, Xinqi Hu, Yu Tian, Gaogao Dong, Renaud Lambiotte, Jianxi Gao, Shlomo Havlin

Abstract: Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between dista… ▽ More Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network's indirect connectivity. This realization leads to the emergence of an alternative theory called ``concurrence percolation,'' which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design. △ Less

Submitted 19 November, 2023; v1 submitted 27 October, 2023; originally announced October 2023.

Hidden Citations Obscure True Impact in Science

Authors: Xiangyi Meng, Onur Varol, Albert-László Barabási

Abstract: References, the mechanism scientists rely on to signal previous knowledge, lately have turned into widely used and misused measures of scientific impact. Yet, when a discovery becomes common knowledge, citations suffer from obliteration by incorporation. This leads to the concept of hidden citation, representing a clear textual credit to a discovery without a reference to the publication embodying… ▽ More References, the mechanism scientists rely on to signal previous knowledge, lately have turned into widely used and misused measures of scientific impact. Yet, when a discovery becomes common knowledge, citations suffer from obliteration by incorporation. This leads to the concept of hidden citation, representing a clear textual credit to a discovery without a reference to the publication embodying it. Here, we rely on unsupervised interpretable machine learning applied to the full text of each paper to systematically identify hidden citations. We find that for influential discoveries hidden citations outnumber citation counts, emerging regardless of publishing venue and discipline. We show that the prevalence of hidden citations is not driven by citation counts, but rather by the degree of the discourse on the topic within the text of the manuscripts, indicating that the more discussed is a discovery, the less visible it is to standard bibliometric analysis. Hidden citations indicate that bibliometric measures offer a limited perspective on quantifying the true impact of a discovery, raising the need to extract knowledge from the full text of the scientific corpus. △ Less

Submitted 11 May, 2024; v1 submitted 24 October, 2023; originally announced October 2023.

Comments: 71 pages (main + SI)

Correcting model misspecification in physics-informed neural networks (PINNs)

Authors: Zongren Zou, Xuhui Meng, George Em Karniadakis

Abstract: Data-driven discovery of governing equations in computational science has emerged as a new paradigm for obtaining accurate physical models and as a possible alternative to theoretical derivations. The recently developed physics-informed neural networks (PINNs) have also been employed to learn governing equations given data across diverse scientific disciplines. Despite the effectiveness of PINNs f… ▽ More Data-driven discovery of governing equations in computational science has emerged as a new paradigm for obtaining accurate physical models and as a possible alternative to theoretical derivations. The recently developed physics-informed neural networks (PINNs) have also been employed to learn governing equations given data across diverse scientific disciplines. Despite the effectiveness of PINNs for discovering governing equations, the physical models encoded in PINNs may be misspecified in complex systems as some of the physical processes may not be fully understood, leading to the poor accuracy of PINN predictions. In this work, we present a general approach to correct the misspecified physical models in PINNs for discovering governing equations, given some sparse and/or noisy data. Specifically, we first encode the assumed physical models, which may be misspecified, then employ other deep neural networks (DNNs) to model the discrepancy between the imperfect models and the observational data. Due to the expressivity of DNNs, the proposed method is capable of reducing the computational errors caused by the model misspecification and thus enables the applications of PINNs in complex systems where the physical processes are not exactly known. Furthermore, we utilize the Bayesian PINNs (B-PINNs) and/or ensemble PINNs to quantify uncertainties arising from noisy and/or gappy data in the discovered governing equations. A series of numerical examples including non-Newtonian channel and cavity flows demonstrate that the added DNNs are capable of correcting the model misspecification in PINNs and thus reduce the discrepancy between the physical models and the observational data. We envision that the proposed approach will extend the applications of PINNs for discovering governing equations in problems where the physico-chemical or biological processes are not well understood. △ Less

Submitted 16 October, 2023; originally announced October 2023.

Scale-Free Networks beyond Power-Law Degree Distribution

Authors: Xiangyi Meng, Bin Zhou

Abstract: Complex networks across various fields are often considered to be scale free -- a statistical property usually solely characterized by a power-law distribution of the nodes' degree $k$. However, this characterization is incomplete. In real-world networks, the distribution of the degree-degree distance $η$, a simple link-based metric of network connectivity similar to $k$, appears to exhibit a stro… ▽ More Complex networks across various fields are often considered to be scale free -- a statistical property usually solely characterized by a power-law distribution of the nodes' degree $k$. However, this characterization is incomplete. In real-world networks, the distribution of the degree-degree distance $η$, a simple link-based metric of network connectivity similar to $k$, appears to exhibit a stronger power-law distribution than $k$. While offering an alternative characterization of scale-freeness, the discovery of $η$ raises a fundamental question: do the power laws of $k$ and $η$ represent the same scale-freeness? To address this question, here we investigate the exact asymptotic {relationship} between the distributions of $k$ and $η$, proving that every network with a power-law distribution of $k$ also has a power-law distribution of $η$, but \emph{not} vice versa. This prompts us to introduce two network models as counterexamples that have a power-law distribution of $η$ but not $k$, constructed using the preferential attachment and fitness mechanisms, respectively. Both models show promising accuracy by fitting only one model parameter each when modeling real-world networks. Our findings suggest that $η$ is a more suitable indicator of scale-freeness and can provide a deeper understanding of the universality and underlying mechanisms of scale-free networks. △ Less

Submitted 20 October, 2023; v1 submitted 12 October, 2023; originally announced October 2023.

Report number: 176

Journal ref: Chaos, Solitons & Fractals, 176, 114173, 2023

Solution multiplicity and effects of data and eddy viscosity on Navier-Stokes solutions inferred by physics-informed neural networks

Authors: Zhicheng Wang, Xuhui Meng, Xiaomo Jiang, Hui Xiang, George Em Karniadakis

Abstract: Physics-informed neural networks (PINNs) have emerged as a new simulation paradigm for fluid flows and are especially effective for inverse and hybrid problems. However, vanilla PINNs often fail in forward problems, especially at high Reynolds (Re) number flows. Herein, we study systematically the classical lid-driven cavity flow at $Re=2,000$, $3,000$ and $5,000$. We observe that vanilla PINNs ob… ▽ More Physics-informed neural networks (PINNs) have emerged as a new simulation paradigm for fluid flows and are especially effective for inverse and hybrid problems. However, vanilla PINNs often fail in forward problems, especially at high Reynolds (Re) number flows. Herein, we study systematically the classical lid-driven cavity flow at $Re=2,000$, $3,000$ and $5,000$. We observe that vanilla PINNs obtain two classes of solutions, one class that agrees with direct numerical simulations (DNS), and another that is an unstable solution to the Navier-Stokes equations and not physically realizable. We attribute this solution multiplicity to singularities and unbounded vorticity, and we propose regularization methods that restore a unique solution within 1\% difference from the DNS solution. In particular, we introduce a parameterized entropy-viscosity method as artificial eddy viscosity and identify suitable parameters that drive the PINNs solution towards the DNS solution. Furthermore, we solve the inverse problem by subsampling the DNS solution, and identify a new eddy viscosity distribution that leads to velocity and pressure fields almost identical to their DNS counterparts. Surprisingly, a single measurement at a random point suffices to obtain a unique PINNs DNS-like solution even without artificial viscosity, which suggests possible pathways in simulating high Reynolds number turbulent flows using vanilla PINNs. △ Less

Submitted 12 September, 2023; originally announced September 2023.

Chaos with Gaussian invariant distribution by quantum-noise random phase feedback

Authors: Yanqiang Guo, Haifeng Li, Yingqi Wang, Xiangyu Meng, Tong Zhao, Xiaomin Guo

Abstract: We experimentally present a random phase feedback based on quantum noise to generate a chaotic laser with Gaussian invariant distribution. The quantum noise from vacuum fluctuations is acquired by balanced homodyne detection and injected into a phase modulator to form a random phase feedback. An optical switch using high-speed intensity modulator is employed to reset the chaotic states repeatedly… ▽ More We experimentally present a random phase feedback based on quantum noise to generate a chaotic laser with Gaussian invariant distribution. The quantum noise from vacuum fluctuations is acquired by balanced homodyne detection and injected into a phase modulator to form a random phase feedback. An optical switch using high-speed intensity modulator is employed to reset the chaotic states repeatedly and the time evolutions of intensity statistical distributions of the chaotic states stemming from the initial noise are measured. By the quantum-noise random phase feedback, the transient intensity distributions of the chaotic outputs are improved from asymmetric invariant distributions to Gaussian invariant distributions, and the Gaussian invariant distribution indicates a randomly perturbed dynamical transition from microscopic initial noise to macroscopic stochastic fluctuation. The effects of phase feedback bandwidth and modulation depth on the invariant distributions are investigated experimentally. The chaotic time-delay signature and mean permutation entropy are suppressed to 0.036 and enhanced to 0.999 using the random phase feedback, respectively. The high-quality chaotic laser with Gaussian invariant distribution can be a desired random source for ultrafast random number generation and secure communication. △ Less

Submitted 12 June, 2023; originally announced June 2023.

Comments: 11 pages, 10 figures

STCF Conceptual Design Report: Volume 1 -- Physics & Detector

Authors: M. Achasov, X. C. Ai, R. Aliberti, L. P. An, Q. An, X. Z. Bai, Y. Bai, O. Bakina, A. Barnyakov, V. Blinov, V. Bobrovnikov, D. Bodrov, A. Bogomyagkov, A. Bondar, I. Boyko, Z. H. Bu, F. M. Cai, H. Cai, J. J. Cao, Q. H. Cao, Z. Cao, Q. Chang, K. T. Chao, D. Y. Chen, H. Chen , et al. (413 additional authors not shown)

Abstract: The Super $τ$-Charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of $0.5\times 10^{35}{\rm cm}^{-2}{\rm s}^{-1}$ or higher. The STCF will produce a data sample about a factor of 100 larger than that by the present $τ$-Charm factory -- the BEPCII,… ▽ More The Super $τ$-Charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of $0.5\times 10^{35}{\rm cm}^{-2}{\rm s}^{-1}$ or higher. The STCF will produce a data sample about a factor of 100 larger than that by the present $τ$-Charm factory -- the BEPCII, providing a unique platform for exploring the asymmetry of matter-antimatter (charge-parity violation), in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions, as well as searching for exotic hadrons and physics beyond the Standard Model. The STCF project in China is under development with an extensive R\&D program. This document presents the physics opportunities at the STCF, describes conceptual designs of the STCF detector system, and discusses future plans for detector R\&D and physics case studies. △ Less

Submitted 5 October, 2023; v1 submitted 28 March, 2023; originally announced March 2023.

Journal ref: Front. Phys. 19(1), 14701 (2024)

Variational inference in neural functional prior using normalizing flows: Application to differential equation and operator learning problems

Authors: Xuhui Meng

Abstract: Physics-informed deep learning have recently emerged as an effective tool for leveraging both observational data and available physical laws. Physics-informed neural networks (PINNs) and deep operator networks (DeepONets) are two such models. The former encodes the physical laws via the automatic differentiation, while the latter learns the hidden physics from data. Generally, the noisy and limite… ▽ More Physics-informed deep learning have recently emerged as an effective tool for leveraging both observational data and available physical laws. Physics-informed neural networks (PINNs) and deep operator networks (DeepONets) are two such models. The former encodes the physical laws via the automatic differentiation, while the latter learns the hidden physics from data. Generally, the noisy and limited observational data as well as the overparameterization in neural networks (NNs) result in uncertainty in predictions from deep learning models. In [1], a Bayesian framework based on the {Generative Adversarial Networks} (GAN) has been proposed as a unified model to quantify uncertainties in predictions of PINNs as well as DeepONets. Specifically, the proposed approach in [1] has two stages: (1) prior learning, and (2) posterior estimation. At the first stage, the GANs are employed to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and available physics. At the second stage, the Hamiltonian Monte Carlo (HMC) method is utilized to estimate the posterior in the latent space of GANs. However, the vanilla HMC does not support the mini-batch training, which limits its applications in problems with big data. In the present work, we propose to use the normalizing flow (NF) models in the context of variational inference, which naturally enables the minibatch training, as the alternative to HMC for posterior estimation in the latent space of GANs. A series of numerical experiments, including a nonlinear differential equation problem and a 100-dimensional Darcy problem, are conducted to demonstrate that NF with full-/mini-batch training are able to achieve similar accuracy as the ``gold rule'' HMC. △ Less

Submitted 21 February, 2023; originally announced February 2023.

Comments: 21 pages, 8 figures

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions

Authors: Zhiping Mao, Xuhui Meng

Abstract: We consider solving the forward and inverse PDEs which have sharp solutions using physics-informed neural networks (PINNs) in this work. In particular, to better capture the sharpness of the solution, we propose adaptive sampling methods (ASMs) based on the residual and the gradient of the solution. We first present a residual only based ASM algorithm denoted by ASM I. In this approach, we first t… ▽ More We consider solving the forward and inverse PDEs which have sharp solutions using physics-informed neural networks (PINNs) in this work. In particular, to better capture the sharpness of the solution, we propose adaptive sampling methods (ASMs) based on the residual and the gradient of the solution. We first present a residual only based ASM algorithm denoted by ASM I. In this approach, we first train the neural network by using a small number of residual points and divide the computational domain into a certain number of sub-domains, we then add new residual points in the sub-domain which has the largest mean absolute value of the residual, and those points which have largest absolute values of the residual in this sub-domain will be added as new residual points. We further develop a second type of ASM algorithm (denoted by ASM II) based on both the residual and the gradient of the solution due to the fact that only the residual may be not able to efficiently capture the sharpness of the solution. The procedure of ASM II is almost the same as that of ASM I except that in ASM II, we add new residual points which not only have large residual but also large gradient. To demonstrate the effectiveness of the present methods, we employ both ASM I and ASM II to solve a number of PDEs, including Burger equation, compressible Euler equation, Poisson equation over an L-shape domain as well as high-dimensional Poisson equation. It has been shown from the numerical results that the sharp solutions can be well approximated by using either ASM I or ASM II algorithm, and both methods deliver much more accurate solution than original PINNs with the same number of residual points. Moreover, the ASM II algorithm has better performance in terms of accuracy, efficiency and stability compared with the ASM I algorithm. △ Less

Submitted 15 February, 2023; originally announced February 2023.

Comments: 22 pages, 9 figures

Machine Learning Assisted Vector Atomic Magnetometry

Authors: Xin Meng, Youwei Zhang, Xichang Zhang, Shenchao Jin, Tingran Wang, Liang Jiang, Liantuan Xiao, Suotang Jia, Yanhong Xiao

Abstract: We propose a novel paradigm to vector magnetometry based on machine learning. Unlike conventional schemes where one measured signal explicitly connects to one parameter, here we encode the three-dimensional magnetic-field information in the set of four simultaneously acquired signals, i.e., the oscillating optical rotation signal's harmonics of a frequency modulated laser beam traversing the atomi… ▽ More We propose a novel paradigm to vector magnetometry based on machine learning. Unlike conventional schemes where one measured signal explicitly connects to one parameter, here we encode the three-dimensional magnetic-field information in the set of four simultaneously acquired signals, i.e., the oscillating optical rotation signal's harmonics of a frequency modulated laser beam traversing the atomic sample. The map between the recorded signals and the vectorial field information is established through a pre-trained deep neural network. We demonstrate experimentally a single-shot all optical vector atomic magnetometer, with a simple scalar-magnetometer design employing only one elliptically-polarized laser beam and no additional coils. Magnetic field amplitude sensitivities of about 100 $\textrm{fT}/\sqrt{\textrm{Hz}}$ and angular sensitivities of about 100 $μrad/\sqrt{\textrm{Hz}}$ (for a magnetic field of about 140 nT) are derived from the neural network. Our approach can reduce the complexity of the architecture of vector magnetometers, and may shed light on the general design of multiparameter sensing. △ Less

Submitted 3 October, 2023; v1 submitted 31 December, 2022; originally announced January 2023.

Comments: 8 pages,4 figures