Showing 1–50 of 71 results for author: Qin, S

Autonomous Generation of Metamaterial Databases Based on Multimodal Agents

Authors: Shilong Qin, Zhicai Yu, Xuan Zheng, Yan Zhang, Aodi Yang, Kezhan Zhao, Qi Cheng Chen, Jian Wei You, Tie Jun Cui

Abstract: Artificial intelligence (AI) is revolutionizing material research and discovery. However, its development in metamaterials is bottlenecked by a shortage of high-quality and executable structure-response databases, which are locked within scientific literatures as a mixture of text and images. Converting the rapidly growing body of scientific literatures into executable and reusable databases for m… ▽ More Artificial intelligence (AI) is revolutionizing material research and discovery. However, its development in metamaterials is bottlenecked by a shortage of high-quality and executable structure-response databases, which are locked within scientific literatures as a mixture of text and images. Converting the rapidly growing body of scientific literatures into executable and reusable databases for machine-driven discovery is still a fundamental challenge. Here, we propose MetaDataGenAgent, a multimodal multi-agent framework that autonomously converts unstructured scientific literatures directly into metamaterial structure-response databases. MetaDataGenAgent establishes a complete literature-to-simulation pipeline through the coordinated operation of specialized agents for multimodal parameter extraction, physics-guided validation, topology-aware structural analysis, and solver-executable encoding. The framework introduces a closed-loop plan-execute-reflect mechanism that enables dynamic task decomposition, iterative validation, and feedback-driven model construction. Experimental results validate that MetaDataGenAgent can generate high-fidelity structure-response data for representative meta-atoms, which are further used to realize diverse electromagnetic functions, including far-field beam deflection, near-field holographic imaging and topologically protected surface-wave transport. By establishing an autonomous route from scientific literatures to AI-ready databases, the framework provides a general and efficient strategy that could be extended to a broad range of data-scarce scientific domains, including photonics, materials science, chemistry, computational science, and scientific automation. △ Less

Submitted 21 June, 2026; originally announced June 2026.

Comments: 18 pages, 5 figures

Seeing full vectorial structures of light fields with a single-shot holographic multiplexed detector

Authors: Sitao Qin, Yize Liang, Shuai Cao, Changqing Cao, Xukun Yin, Lixian Liu, Mingjian Cheng, Huailiang Xu

Abstract: The vectorial structure of light, amplitude, phase, and polarization, encodes essential information for applications ranging from super-resolution microscopy to high-capacity communications and quantum information processing. However, existing characterization methods either rely on multiple sequential measurements or require bulky polarization splitting optics in the signal path. Here we propose… ▽ More The vectorial structure of light, amplitude, phase, and polarization, encodes essential information for applications ranging from super-resolution microscopy to high-capacity communications and quantum information processing. However, existing characterization methods either rely on multiple sequential measurements or require bulky polarization splitting optics in the signal path. Here we propose and experimentally demonstrate a single shot holographic multiplexed detector that retrieves the full vectorial information from a single intensity recording. Two orthogonally polarized reference beams with distinct off axis carriers interfere with the unknown vectorial light field, encoding both polarization channels into one off axis hologram. Digital holographic reconstruction combined with a self calibrated global phase retrieval recovers the complex wavefronts in the two channels without any additional measurements. We validate our approach by characterizing the polarization structures and concurrence of various vectorial structured light beams on a higher order Poincare sphere (l=2). This compact, efficient detector may open new routes for real time vectorial metrology in light matter interaction, chiral sensing, vectorial adaptive optics, and dynamic structured light applications. △ Less

Submitted 28 April, 2026; originally announced April 2026.

Non-uniqueness of smooth solutions of the Navier-Stokes equations from almost the same initial conditions

Authors: Shijun Liao, Shijie Qin

Abstract: Using clean numerical simulation (CNS) which can give very accurate spatiotemporal trajectory of Navier-Stokes turbulence in a finite but long enough interval of time, we give some numerical evidences that the Navier-Stokes equations admit distinct global solutions from almost the same initial conditions whose difference is very small, i.e. even at the order $10^{-40}$ of magnitude. Hopefully thes… ▽ More Using clean numerical simulation (CNS) which can give very accurate spatiotemporal trajectory of Navier-Stokes turbulence in a finite but long enough interval of time, we give some numerical evidences that the Navier-Stokes equations admit distinct global solutions from almost the same initial conditions whose difference is very small, i.e. even at the order $10^{-40}$ of magnitude. Hopefully these examples could provide some enlightenments for the uniqueness and existence of Navier-Stokes equations, which are related to one Millennium Prize Problem of Clay Institute. △ Less

Submitted 13 February, 2026; originally announced February 2026.

Comments: 8 pages, 3 figures

Geese achieve stationary takeoff via synergistic wing kinematics and enhanced aerodynamics

Authors: Jinpeng Huang, Yang Xiang, Lunbing Chen, Suyang Qin, Jixin Lu, Sen Ye, Yong Chen, Hong Liu

Abstract: Stationary take-off, without a running start or elevated descent, requires substantial aerodynamic forces to overcome weight, particularly for large birds such as geese exceeding 2 kg. However, the complex wing motion and high-Reynolds-number (Re $\approx$$10^5$) flow dynamics challenge conventional expectations of avian flight aerodynamics, rendering this mechanism elusive. Analyzing 578 stationa… ▽ More Stationary take-off, without a running start or elevated descent, requires substantial aerodynamic forces to overcome weight, particularly for large birds such as geese exceeding 2 kg. However, the complex wing motion and high-Reynolds-number (Re $\approx$$10^5$) flow dynamics challenge conventional expectations of avian flight aerodynamics, rendering this mechanism elusive. Analyzing 578 stationary take-offs from seven geese (\textit{Anser cygnoides}) and applying Principal Component Analysis (PCA), we reveal that the complex wing kinematics collapse onto a low-dimensional manifold dominated by two synergies: a Stroke Synergy responsible for fundamental rhythmic stroke, and a Morphing Synergy governing spanwise geometry. This modular control strategy orchestrates a stereotyped wing kinematics featuring an accelerated translational downstroke and a rapid tip-reversal upstroke. By integrating wing kinematic analysis with the mass distribution of the geese, we quantified the aerodynamic forces and found that entirely positive lift and thrust are generated throughout the motion cycle. The enhanced aerodynamic performance of geese takeoff results from three principal mechanisms. During the downstroke, significant lift generated from wing acceleration is predicted by the quasi-steady framework. Flow visualization reveals that wake capture further enhances the lift generation in downstroke by orienting the position of wake vortices. During the upstroke, the distal wing performs a rapid pitching motion and generates a substantial thrust, the vertical component of which contributes significantly to weight support. △ Less

Submitted 23 December, 2025; originally announced December 2025.

Comments: 17 pages, 4 figures, to be submitted to a journal

A Fixed-Volume Variant of Gibbs-Ensemble Monte Carlo Yields Significant Speedup in Binodal Calculation

Authors: Sanbo Qin, Huan-Xiang Zhou

Abstract: Gibbs-ensemble Monte Carlo (GEMC) is a powerful method for calculating the gas-liquid binodals of simple models and small molecules, but is too demanding computationally for realistic models of proteins. Here we discover that the main reason for long simulations is that volume exchange is very slow to achieve, and develop a variant GEMC without volume exchange. The key is to determine an appropria… ▽ More Gibbs-ensemble Monte Carlo (GEMC) is a powerful method for calculating the gas-liquid binodals of simple models and small molecules, but is too demanding computationally for realistic models of proteins. Here we discover that the main reason for long simulations is that volume exchange is very slow to achieve, and develop a variant GEMC without volume exchange. The key is to determine an appropriate initial density. Test of this fixed-volume GEMC method on Lennard-Jones and patchy particles shows enormous speedup without any loss of accuracy in predicted binodals. The fast speed of fixed-volume GEMC promises many applications. △ Less

Submitted 21 December, 2025; originally announced December 2025.

Comments: 17 pages, 7 figures

A 3D-integrated BiCMOS-silicon photonics high-speed receiver realized using micro-transfer printing

Authors: Ye Gu, He Li, Tinus Pannier, Shengpu Niu, Patrick Heise, Christian Mai, Prasanna Ramaswamy, Alex Farrel, Alin Fecioru, Antonio Jose Trindade, Ruggero Loi, Nishant Singh, Senbiao Qin, Biwei Pan, Jing Zhang, Johanna Rimbock, Kristof Dhaenens, Toon De Baere, Geert Van Steenberge, Dieter Bode, Dimitrios Velenis, Guy Lepage, Neha Singh, Joris Van Campenhout, Xin Yin , et al. (2 additional authors not shown)

Abstract: Meeting the escalating demands of data transmission and computing, driven by artificial intelligence (AI), requires not only faster optical transceivers but also advanced integration technologies that can seamlessly combine photonic and electronic components. Traditional approaches struggle to overcome the parasitic limitations arising from fabricating those components using different processes. H… ▽ More Meeting the escalating demands of data transmission and computing, driven by artificial intelligence (AI), requires not only faster optical transceivers but also advanced integration technologies that can seamlessly combine photonic and electronic components. Traditional approaches struggle to overcome the parasitic limitations arising from fabricating those components using different processes. Here, we report a novel 3D heterogeneously integrated optical receiver based on micro-transfer printing (μTP), enabling the co-integration of a compact bipolar CMOS (BiCMOS) electronic chiplet (0.06 mm2) directly onto a silicon photonic integrated circuit (SiPIC). While previous μTP demonstrations have focused primarily on photonic integration, our work pioneers the direct integration of electronics and photonics, significantly enhancing performance and scalability. The resulting optical receiver achieves 224 Gb/s four-level pulse amplitude modulation (PAM-4) operation, delivering -5.2 dBm optical modulation amplitude(OMA) sensitivity at a bit-error rate (BER) of 2.4 x 10-4, a record-small footprint, and an excellent power efficiency of 0.51 pJ/b. This demonstration not only showcases the potential of μTP for high-density, cost-efficient integration but also represents a critical step toward next-generation optical interconnects in the AI era. △ Less

Submitted 28 November, 2025; originally announced November 2025.

Reply to the comments of McMullen et al. (arXiv:2510.04828)

Authors: Shijun Liao, Shijie Qin

Abstract: McMullen et al. [1] comment that the numerical simulations that explicitly include random velocity fluctuations ``should exhibit a thermal-fluctuation-dominated range'' consistent with the literature, so that our results (J. Fluid Mech. 1008, R2, 2025) [2] ``contradict other results in the literature''. First of all, we would give an opposite example against this viewpoint: DNS results (that are b… ▽ More McMullen et al. [1] comment that the numerical simulations that explicitly include random velocity fluctuations ``should exhibit a thermal-fluctuation-dominated range'' consistent with the literature, so that our results (J. Fluid Mech. 1008, R2, 2025) [2] ``contradict other results in the literature''. First of all, we would give an opposite example against this viewpoint: DNS results (that are badly polluted by numerical noises quickly, as mention in Section 2) implicitly include random numerical noises, but they also DO NOT exhibit a thermal-fluctuation-dominated range. In other words, DNS results in the literature qualitatively agree with ours at this point. In addition, we highly suggest that influences of numerical noises on statistics of turbulent flows given by ALL numerical approaches should be carefully checked, since numerical noises might have huge influences on statistics of chaotic systems (including turbulence), as pointed by Lorenz [3] in 2006. Detailed replies are given below. △ Less

Submitted 10 October, 2025; originally announced October 2025.

Comments: 6 pages

A paradox of the Navier-Stokes turbulence

Authors: Shijie Qin, Kun Xu, Shijun Liao

Abstract: The Navier-Stokes (NS) equations as a turbulence model have been widely applied in lots of fields. The NS equations contain such a fundamental assumption that all small physical/artificial disturbances could be neglected. Is this assumption correct? In this paper a two-dimensional Rayleigh-Bénard convection governed by the NS equations is predicted by traditional direct numerical simulation (DNS)… ▽ More The Navier-Stokes (NS) equations as a turbulence model have been widely applied in lots of fields. The NS equations contain such a fundamental assumption that all small physical/artificial disturbances could be neglected. Is this assumption correct? In this paper a two-dimensional Rayleigh-Bénard convection governed by the NS equations is predicted by traditional direct numerical simulation (DNS) using double precision arithmetic and a range of different time-steps. It is found that the final flow type tends either to vortical flow or zonal flow, whose statistics are completely different. Notably, these two flow types frequently alternate as the time-step is reduced to a very small value, suggesting that the time-step corresponding to each turbulent flow type should be densely distributed. Thus, stochastic numerical noise exerts a huge influence on the final flow type and statistics of numerically simulated NS turbulence because the time-step has a close relationship with numerical noise. This clearly indicates that small disturbances have significant influences on the NS turbulence, which therefore should not be neglected. This leads to a logical paradox for the NS turbulence, which is a great challenge for us, although a paradox often leads to some significant breakthroughs. △ Less

Submitted 26 April, 2026; v1 submitted 13 October, 2025; originally announced October 2025.

Comments: 13 page, 4 figures

Ultra-chaotic property of Navier-Stokes turbulence

Authors: Shijie Qin, Kun Xu, Shijun Liao

Abstract: A chaotic system is called ultra-chaos when its statistics have sensitivity dependence on initial condition and/or other small disturbances. In this paper, using two-dimensional turbulent Kolmogorov flow as an example, we illustrate that tiny variation of initial condition of Navier-Stokes equations can lead to huge differences not only in spatiotemporal trajectory but also in flow symmetry and it… ▽ More A chaotic system is called ultra-chaos when its statistics have sensitivity dependence on initial condition and/or other small disturbances. In this paper, using two-dimensional turbulent Kolmogorov flow as an example, we illustrate that tiny variation of initial condition of Navier-Stokes equations can lead to huge differences not only in spatiotemporal trajectory but also in flow symmetry and its statistics. Here, in order to avoid the influence of artificial numerical noise, we apply ``clean numerical simulation'' (CNS) which can guarantee that the numerical noise can be reduced to such a desired low level that they are negligible in a time interval long enough for calculating statistics. This discovery highly suggests that the Navier-Stokes turbulence (i.e. turbulence governed by the Navier-Stokes equations) might be an ultra-chaos, say, small disturbances must be considered even from viewpoint of statistics. This however leads to a paradox in logic, since small disturbances, which are unavoidable in practice, are unfortunately neglected by the Navier-Stokes turbulence. Some fundamental characteristics of turbulence model are discussed and suggested in general meanings. △ Less

Submitted 8 October, 2025; originally announced October 2025.

Comments: 12 pages, 6 figures

Journal ref: European Journal of Mechanics / B Fluids 119 (2026) 204540

Passive aerodynamic robustness reduces disturbance amplification in flight

Authors: Lunbing Chen, Suyang Qin, Qilin Wu, Jinpeng Huang, Yufei Yin, Yong Chen, Yang Xiang, Hong Liu

Abstract: Flight in turbulence is constrained not only by aerodynamic efficiency, but also by how strongly flow disturbances are transmitted into unsteady loads and dynamic responses. Although disturbance rejection is typically attributed to active control, birds often sustain fixed-wing gliding in disturbed air, suggesting that the wing itself may passively attenuate aerodynamic perturbations. Here, we sho… ▽ More Flight in turbulence is constrained not only by aerodynamic efficiency, but also by how strongly flow disturbances are transmitted into unsteady loads and dynamic responses. Although disturbance rejection is typically attributed to active control, birds often sustain fixed-wing gliding in disturbed air, suggesting that the wing itself may passively attenuate aerodynamic perturbations. Here, we show that avian wings reduce aerodynamic sensitivity to incoming disturbances. Compared with a geometrically matched airfoil wing, the avian wing exhibits lower lift-response gain, smoother stall transition, reduced force fluctuations, and a broader operative angle-of-attack range across turbulence intensities. These wing-level properties translate into an expanded passive stability envelope in rigid-flyer dynamics. Flow diagnostics indicate that this robustness is associated with delayed separation and redistribution of turbulent kinetic energy, which suppress large-scale flow instability and weaken disturbance transmission. This passive robustness comes at the cost of reduced aerodynamic efficiency, revealing an efficiency-robustness trade-off in disturbed flows. Our results identify aerodynamic sensitivity and control demand as essential metrics for flight performance in turbulence, and suggest passive aerodynamic robustness as a design principle for resilient flying systems. △ Less

Submitted 5 June, 2026; v1 submitted 19 July, 2025; originally announced July 2025.

Clean numerical simulation (CNS) of three-dimensional turbulent Kolmogorov flow

Authors: Shijie Qin, Shijun Liao

Abstract: Turbulence holds immense importance across various scientific and engineering disciplines. The direct numerical simulation (DNS) of turbulence proposed by Orszag in 1970 is a milestone in fluid mechanics, which began an era of numerical experiment for turbulence. Many researchers have reported that turbulence should be chaotic, since spatiotemporal trajectories are very sensitive to small disturba… ▽ More Turbulence holds immense importance across various scientific and engineering disciplines. The direct numerical simulation (DNS) of turbulence proposed by Orszag in 1970 is a milestone in fluid mechanics, which began an era of numerical experiment for turbulence. Many researchers have reported that turbulence should be chaotic, since spatiotemporal trajectories are very sensitive to small disturbance. Thus, due to the famous butterfly-effect of chaos, unavoidable numerical noises of DNS might have great influence on spatiotemporal trajectories of turbulence. This is indeed true for a two-dimensional (2D) Kolmogorov turbulent flow, as currently revealed by a much more accurate algorithm than DNS, namely the ``clean numerical simulation'' (CNS). Different from DNS, CNS can greatly reduce both of truncation error and round-off error to any required small level so that numerical noise can be rigorously negligible throughout a time interval long enough for calculating statistics. However, In physics, 3D turbulent flow is more important than 2D turbulence. Thus, for the first time, we solve a 3D turbulent Kolmogorov flow by means of CNS in this paper, and compare our CNS result with that given by DNS in details. It is found that the spatial-temporal trajectories of the 3D Kolmogorov turbulent flow given by DNS are indeed badly polluted by numerical noise rather quickly, and besides the DNS result has significant deviations from the CNS benchmark solution not only in the spatial symmetry of flow field and the energy cascade but also even in statistics. △ Less

Submitted 13 October, 2025; v1 submitted 15 July, 2025; originally announced July 2025.

Comments: 29 pages, 11 figures

Journal ref: Phys. Fluids 37, 105160 (2025)

Programmable skyrmions for robust communication and intelligent sensing

Authors: Long Chen, Xin Yu Li, Yijie Shen, Ze Gu, Jian Lin Su, Qiang Xiao, Si Qi Huang, Shi Long Qin, Qian Ma, Jian Wei You, Tie Jun Cui

Abstract: The recently observed plasmonic skyrmions, as electromagnetic counterparts of topologically stable quasiparticles, hold significant promise as novel carriers for robust information transfer and manipulation of nontrivial light-matter interactions. However, their practical applications has been hindered by the lack of flexible tuning devices to encode these topological structures. Here, we present… ▽ More The recently observed plasmonic skyrmions, as electromagnetic counterparts of topologically stable quasiparticles, hold significant promise as novel carriers for robust information transfer and manipulation of nontrivial light-matter interactions. However, their practical applications has been hindered by the lack of flexible tuning devices to encode these topological structures. Here, we present a programmable plasmonic skyrmion platform capable of encoding diverse skyrmion topologies, including Neel-type skyrmions and merons. Based on unprecedented ultra-fast coding feature, we synthesize harmonic skyrmions in the temporal dimension and, for the first time, applied skyrmions in communication and sensing applications. Specifically, we achieved highly robust and multi-channel wireless communications by using programmable topological skyrmions, providing a promising platform for communication in turbulent noise channels and extreme conditions. Furthermore, we implemented intelligent sensing across twenty animal models on the same platform, achieving high recognition accuracy. This methodology offers programmable and temporal insights into the skyrmions for their practical applications in next-generation wireless communication and intelligent sensing. △ Less

Submitted 14 September, 2025; v1 submitted 8 July, 2025; originally announced July 2025.

Topological Optical Achirality

Authors: C. Wen, Z. Qi, J. Zhang, C. Zhang, S. Qin, Z. Zhu, W. Liu

Abstract: For arbitrary reciprocal single-mode structures, regardless of their geometric shapes or constituent materials, there must exist incident directions of plane waves for which they are optically achiral. For arbitrary reciprocal single-mode structures, regardless of their geometric shapes or constituent materials, there must exist incident directions of plane waves for which they are optically achiral. △ Less

Submitted 1 July, 2025; originally announced July 2025.

Comments: Four pages and three figures; Comments to welcome

MXene triggers high toughness, high strength and low hysteresis hydrogels for printed artificial tissue

Authors: Chendong Zhao, Yaxing Li, Qinglong He, Shangpeng Qin, Huiqi Xie, Chuanfang Zhang

Abstract: Substituting load-bearing tissues requires hydrogels with rapid processability, excellent mechanical strength and fatigue resistance. Conventional homogeneously polymerized hydrogels with short-chains/excessive branching exhibit low strength/toughness, being inadequate for artificial tissues. Here we introduce the heterogeneous polymerization-accelerated reaction kinetics on the Ti3C2Tx MXene micr… ▽ More Substituting load-bearing tissues requires hydrogels with rapid processability, excellent mechanical strength and fatigue resistance. Conventional homogeneously polymerized hydrogels with short-chains/excessive branching exhibit low strength/toughness, being inadequate for artificial tissues. Here we introduce the heterogeneous polymerization-accelerated reaction kinetics on the Ti3C2Tx MXene microreactor and sluggish kinetics beyond-to rapidly produce hydrogels within minutes. This allows the hyperbranched domains embedded within a highly entangled matrix, leading to excellent strength (2.4 MPa)/toughness (75.2 kJ m-2) and low hysteresis (2.9%) in hydrogels superior to the rest ones. The rapid liquid-to-solid transition triggered by MXene suggests the great possibility of 3D printed robust hydrogels toward artificial tissue. Importantly, these printed hydrogels-based artificial ligaments have demonstrated impressive load-bearing capacity, wear resistance, and suturability compared to commercial analogs. △ Less

Submitted 15 June, 2025; originally announced June 2025.

Polarization-Independent Zero Directional Scattering Without Geometric Symmetries

Authors: Chunchao Wen, Zhichun Qi, Jianfa Zhang, Shiqiao Qin, Zhihong Zhu, Wei Liu

Abstract: As the characteristic feature of generalized Kerker effect in Mie theory, directional scattering elimination has been playing a pivotal role in nanophotonics and many other photonic disciplines, such as singular optics and topological photonics. Generally, zero directional scattering can be obtained only for a specific incident polarization, and to make it fully independent of arbitrary polarizati… ▽ More As the characteristic feature of generalized Kerker effect in Mie theory, directional scattering elimination has been playing a pivotal role in nanophotonics and many other photonic disciplines, such as singular optics and topological photonics. Generally, zero directional scattering can be obtained only for a specific incident polarization, and to make it fully independent of arbitrary polarizations would require scatterers that exhibit geometric (\textit{e.g.} mirror) symmetries. Here we revisit the generalized Kerker effect and directional scattering elimination from the perspective of not the conventional electromagnetic multipoles, but rather quasi-normal modes supported by non-Hermitian systems. We reveal how to obtain zero directional scattering that is independent of arbitrary incident polarizations, even for scattering structures that do not exhibit the required geometric symmetries. Such geometric symmetry-free and polarization-independent responses are made accessible through a synchronous exploitation of electromagnetic reciprocity and geometric phase. Our discovery can stimulate fundamental explorations and practical applications in not only photonics, but also many other wave physics branches where scattering and geometric phase are pervasive. △ Less

Submitted 27 May, 2025; originally announced May 2025.

Comments: 6 pages and 6 figures; Comments to Welcome

Physical significance of artificial numerical noise in direct numerical simulation of turbulence

Authors: Shijun Liao, Shijie Qin

Abstract: Using clean numerical simulation (CNS) in which artificial numerical noise is negligible over a finite, sufficiently long interval of time, we provide evidence, for the first time, that artificial numerical noise in direct numerical simulation (DNS) of turbulence is approximately equivalent to thermal fluctuation and/or stochastic environmental noise. This confers physical significance on the arti… ▽ More Using clean numerical simulation (CNS) in which artificial numerical noise is negligible over a finite, sufficiently long interval of time, we provide evidence, for the first time, that artificial numerical noise in direct numerical simulation (DNS) of turbulence is approximately equivalent to thermal fluctuation and/or stochastic environmental noise. This confers physical significance on the artificial numerical noise of DNS of the Navier-Stokes equations. As a result, DNS on a fine mesh should correspond to turbulence under small internal/external physical disturbance, whereas DNS on a sparse mesh corresponds to turbulent flow under large physical disturbance, respectively. The key point is that: all of them have physical meanings and so are correct in terms of their deterministic physics, even if their statistics are quite different. This is illustrated herein. Our paper provides a positive viewpoint regarding the presence of artificial numerical noise in DNS. △ Less

Submitted 4 April, 2025; originally announced April 2025.

Comments: 16 pages, 12 figures

Journal ref: Journal of Fluid Mechanics (2025), vol. 1008, R2

FLUME-FNO: data-efficient and scalable prediction of 3D wind and temperature fields in unseen urban morphologies

Authors: Shaoxiang Qin, Theodore Potsis, Dongxue Zhan, Xue Liu, Ted Stahopoulos, Liangzhu Leon Wang

Abstract: Urban microclimate, encompassing wind and temperature fields shaped by building geometry, significantly impacts energy consumption, pedestrian winds, pollutant dispersion, urban heat island, and public health. Accurately predicting microclimate is crucial yet challenging. Conventional Computational Fluid Dynamics (CFD) is computationally prohibitive for rapid assessments, while many deep learning… ▽ More Urban microclimate, encompassing wind and temperature fields shaped by building geometry, significantly impacts energy consumption, pedestrian winds, pollutant dispersion, urban heat island, and public health. Accurately predicting microclimate is crucial yet challenging. Conventional Computational Fluid Dynamics (CFD) is computationally prohibitive for rapid assessments, while many deep learning approaches require extensive training data and struggle with generalization in unseen configurations. We present the Fast Localized Urban Microclimate Emulation Fourier Neural Operator (FLUME-FNO), a data-efficient and scalable framework for rapid prediction of 3D wind and temperature fields based solely on building geometry. FLUME-FNO assumes the local urban microclimate is primarily governed by surrounding geometry directly visible from a specific location. To encode this, the framework introduces a novel Multi-Directional Distance Feature (MDDF), representing visible open-space structures by measuring directional distances to surrounding buildings. By computing MDDF over the full domain and cropping encoded geometric features into smaller 3D patches, FLUME-FNO effectively augments limited CFD data, enabling robust learning from just 23 CFD simulations. The model achieves mean absolute errors of 0.2 m/s for wind speed and 0.19 °C for temperature on unseen configurations. Addressing the need for trustworthy fast microclimate prediction, the framework is further assessed using a deep ensemble as a practical proxy for FLUME-FNO uncertainty, ranging from 3% to 40% depending on location. The UQ framework demonstrates FLUME-FNO provides resilient, trustworthy predictions within acceptable accuracy thresholds for wind engineering and microclimate studies, highlighting its potential for real-world applications. △ Less

Submitted 19 May, 2026; v1 submitted 25 March, 2025; originally announced March 2025.

Generalization of Urban Wind Environment Using Fourier Neural Operator Across Different Wind Directions and Cities

Authors: Cheng Chen, Geng Tian, Shaoxiang Qin, Senwen Yang, Dingyang Geng, Dongxue Zhan, Jinqiu Yang, David Vidal, Liangzhu Leon Wang

Abstract: Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow field… ▽ More Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow fields under different wind directions and urban layouts. In this study, we investigate the effectiveness of the Fourier Neural Operator (FNO) model in predicting urban wind conditions under different wind directions and urban layouts. By training the model on velocity data from large eddy simulation data, we evaluate the performance of the model under different urban configurations and wind conditions. The results show that the FNO model can provide accurate predictions while significantly reducing the computational time by 99%. Our innovative approach of dividing the wind field into smaller spatial blocks for training improves the ability of the FNO model to capture wind frequency features effectively. The SDF data also provides important spatial building information, enhancing the model's ability to recognize physical boundaries and generate more realistic predictions. The proposed FNO approach enhances the AI model's generalizability for different wind directions and urban layouts. △ Less

Submitted 9 January, 2025; originally announced January 2025.

Using Diffusion Models for Reducing Spatiotemporal Errors of Deep Learning Based Urban Microclimate Predictions at Post-Processing Stage

Authors: Sepehrdad Tahmasebi, Geng Tian, Shaoxiang Qin, Ahmed Marey, Liangzhu Leon Wang, Saeed Rayegan

Abstract: Computational fluid dynamics (CFD) is a powerful tool for modeling turbulent flow and is commonly used for urban microclimate simulations. However, traditional CFD methods are computationally intensive, requiring substantial hardware resources for high-fidelity simulations. Deep learning (DL) models are becoming popular as efficient alternatives as they require less computational resources to mode… ▽ More Computational fluid dynamics (CFD) is a powerful tool for modeling turbulent flow and is commonly used for urban microclimate simulations. However, traditional CFD methods are computationally intensive, requiring substantial hardware resources for high-fidelity simulations. Deep learning (DL) models are becoming popular as efficient alternatives as they require less computational resources to model complex non-linear interactions in fluid flow simulations. A major drawback of DL models is that they are prone to error accumulation in long-term temporal predictions, often compromising their accuracy and reliability. To address this shortcoming, this study investigates the use of a denoising diffusion probabilistic model (DDPM) as a novel post-processing technique to mitigate error propagation in DL models' sequential predictions. To address this, we employ convolutional autoencoder (CAE) and U-Net architectures to predict airflow dynamics around a cubic structure. The DDPM is then applied to the models' predictions, refining the reconstructed flow fields to better align with high-fidelity statistical results obtained from large-eddy simulations. Results demonstrate that, although deep learning models provide significant computational advantages over traditional numerical solvers, they are susceptible to error accumulation in sequential predictions; however, utilizing DDPM as a post-processing step enhances the accuracy of DL models by up to 65% while maintaining a 3 times speedup compared to traditional numerical solvers. These findings highlight the potential of integrating denoising diffusion probabilistic models as a transformative approach to improving the reliability and accuracy of deep learning-based urban microclimate simulations, paving the way for more efficient and scalable fluid dynamics modeling. △ Less

Submitted 8 January, 2025; originally announced January 2025.

A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy

Authors: Zilin Wang, Shengqi Chen, Jianrong Dai, Shirui Qin, Ying Cao, Ruiao Zhao, Guohua Wu, Yuan Tang, Jiayun Chen

Abstract: Objective:To develop a no-reference image quality assessment method using automated distortion recognition to boost MRI-guided radiotherapy precision.Methods:We analyzed 106,000 MR images from 10 patients with liver metastasis,captured with the Elekta Unity MR-LINAC.Our No-Reference Quality Assessment Model includes:1)image preprocessing to enhance visibility of key diagnostic features;2)feature e… ▽ More Objective:To develop a no-reference image quality assessment method using automated distortion recognition to boost MRI-guided radiotherapy precision.Methods:We analyzed 106,000 MR images from 10 patients with liver metastasis,captured with the Elekta Unity MR-LINAC.Our No-Reference Quality Assessment Model includes:1)image preprocessing to enhance visibility of key diagnostic features;2)feature extraction and directional analysis using MSCN coefficients across four directions to capture textural attributes and gradients,vital for identifying image features and potential distortions;3)integrative Quality Index(QI)calculation,which integrates features via AGGD parameter estimation and K-means clustering.The QI,based on a weighted MAD computation of directional scores,provides a comprehensive image quality measure,robust against outliers.LOO-CV assessed model generalizability and performance.Tumor tracking algorithm performance was compared with and without preprocessing to verify tracking accuracy enhancements.Results:Preprocessing significantly improved image quality,with the QI showing substantial positive changes and surpassing other metrics.After normalization,the QI's average value was 79.6 times higher than CNR,indicating improved image definition and contrast.It also showed higher sensitivity in detail recognition with average values 6.5 times and 1.7 times higher than Tenengrad gradient and entropy.The tumor tracking algorithm confirmed significant tracking accuracy improvements with preprocessed images,validating preprocessing effectiveness.Conclusions:This study introduces a novel no-reference image quality evaluation method based on automated distortion recognition,offering a new quality control tool for MRIgRT tumor tracking.It enhances clinical application accuracy and facilitates medical image quality assessment standardization, with significant clinical and research value. △ Less

Submitted 9 December, 2024; v1 submitted 9 December, 2024; originally announced December 2024.

Modeling Multivariable High-resolution 3D Urban Microclimate Using Localized Fourier Neural Operator

Authors: Shaoxiang Qin, Dongxue Zhan, Dingyang Geng, Wenhui Peng, Geng Tian, Yurong Shi, Naiping Gao, Xue Liu, Liangzhu Leon Wang

Abstract: Accurate urban microclimate analysis with wind velocity and temperature is vital for energy-efficient urban planning, supporting carbon reduction, enhancing public health and comfort, and advancing the low-altitude economy. However, traditional computational fluid dynamics (CFD) simulations that couple velocity and temperature are computationally expensive. Recent machine learning advancements off… ▽ More Accurate urban microclimate analysis with wind velocity and temperature is vital for energy-efficient urban planning, supporting carbon reduction, enhancing public health and comfort, and advancing the low-altitude economy. However, traditional computational fluid dynamics (CFD) simulations that couple velocity and temperature are computationally expensive. Recent machine learning advancements offer promising alternatives for accelerating urban microclimate simulations. The Fourier neural operator (FNO) has shown efficiency and accuracy in predicting single-variable velocity magnitudes in urban wind fields. Yet, for multivariable high-resolution 3D urban microclimate prediction, FNO faces three key limitations: blurry output quality, high GPU memory demand, and substantial data requirements. To address these issues, we propose a novel localized Fourier neural operator (Local-FNO) model that employs local training, geometry encoding, and patch overlapping. Local-FNO provides accurate predictions for rapidly changing turbulence in urban microclimate over 60 seconds, four times the average turbulence integral time scale, with an average error of 0.35 m/s in velocity and 0.30 °C in temperature. It also accurately captures turbulent heat flux represented by the velocity-temperature correlation. In a 2 km by 2 km domain, Local-FNO resolves turbulence patterns down to a 10 m resolution. It provides high-resolution predictions with 150 million feature dimensions on a single 32 GB GPU at nearly 50 times the speed of a CFD solver. Compared to FNO, Local-FNO achieves a 23.9% reduction in prediction error and a 47.3% improvement in turbulent fluctuation correlation. △ Less

Submitted 18 November, 2024; originally announced November 2024.

A Novel Automatic Real-time Motion Tracking Method in MRI-guided Radiotherapy Using Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation

Authors: Shengqi Chen, Zilin Wang, Jianrong Dai, Shirui Qin, Ying Cao, Ruiao Zhao, Jiayun Chen, Guohua Wu, Yuan Tang

Abstract: Background and Purpose: Accurate motion tracking in MRI-guided Radiotherapy (MRIgRT) is essential for effective treatment delivery. This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework with automatic segmentation. Materials and Methods: We developed a novel MRIgRT mot… ▽ More Background and Purpose: Accurate motion tracking in MRI-guided Radiotherapy (MRIgRT) is essential for effective treatment delivery. This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework with automatic segmentation. Materials and Methods: We developed a novel MRIgRT motion tracking and segmentation method by integrating the ETLD framework with an improved Chan-Vese model (ICV), named ETLD+ICV. The ETLD framework was upgraded for real-time cine MRI, including advanced image preprocessing, no-reference image quality assessment, an enhanced median-flow tracker, and a refined detector with dynamic search region adjustments. ICV was used for precise target volume coverage, refining the segmented region frame by frame using tracking results, with key parameters optimized. The method was tested on 3.5D MRI scans from 10 patients with liver metastases. Results: Evaluation of 106,000 frames across 77 treatment fractions showed sub-millimeter tracking errors of less than 0.8mm, with over 99% precision and 98% recall for all subjects in the Beam Eye View(BEV)/Beam Path View(BPV) orientation. The ETLD+ICV method achieved a dice global score of more than 82% for all subjects, demonstrating the method's extensibility and precise target volume coverage. Conclusion: This study successfully developed an automatic real-time markerless motion tracking method for MRIgRT that significantly outperforms current methods. The novel method not only delivers exceptional precision in tracking and segmentation but also shows enhanced adaptability to clinical demands, making it an indispensable asset in improving the efficacy of radiotherapy treatments. △ Less

Submitted 7 July, 2025; v1 submitted 11 November, 2024; originally announced November 2024.

Binding memory of liquid molecules

Authors: Shiyi Qin, Zhi Yang, Huimin Liu, Xiaoli Wang, Shangguo Hou, Kai Huang

Abstract: Understanding the binding dynamics of liquid molecules is of fundamental importance in physical and life sciences. However, nanoscale fast dynamics pose great challenges for experimental characterization. Conventionally, the binding dynamics have been assumed to be memoryless. Here, we integrate large scale computer simulation, scaling theory, and real-time single particle tracking microscopy with… ▽ More Understanding the binding dynamics of liquid molecules is of fundamental importance in physical and life sciences. However, nanoscale fast dynamics pose great challenges for experimental characterization. Conventionally, the binding dynamics have been assumed to be memoryless. Here, we integrate large scale computer simulation, scaling theory, and real-time single particle tracking microscopy with high spatiotemporal precision to unveil a universal memory effect in the binding dynamics of liquid molecules. This binding memory can be quantified by a binding time autocorrelation function, whose power-law decay depends not only on the binding affinity, but also on the topological and materials properties of the surrounding environment. Context-dependent biomolecular binding memory is likely exploited by biological systems to regulate biochemical reactions and biophysical processes. Deciphering this binding memory offers a novel strategy to probe complex biological systems and advanced soft materials. △ Less

Submitted 25 October, 2024; originally announced October 2024.

Noise-expansion cascade: an origin of randomness of turbulence

Authors: Shijun Liao, Shijie Qin

Abstract: Randomness is one of the most important characteristics of turbulence, but its origin remains an open question. By means of a ``thought experiment'' via several clean numerical experiments based on the Navier-Stokes equations for two-dimensional turbulent Kolmogorov flow, we reveal a new phenomenon, which we call the ``noise-expansion cascade'' whereby all micro-level noises/disturbances at differ… ▽ More Randomness is one of the most important characteristics of turbulence, but its origin remains an open question. By means of a ``thought experiment'' via several clean numerical experiments based on the Navier-Stokes equations for two-dimensional turbulent Kolmogorov flow, we reveal a new phenomenon, which we call the ``noise-expansion cascade'' whereby all micro-level noises/disturbances at different orders of magnitudes in the initial condition of Navier-Stokes equations enlarge consistently, say, one by one like an inverse cascade, to macro-level. More importantly, each noise/disturbance input may greatly change the macro-level characteristics and statistics of the resulting turbulence, clearly indicating that micro-level noise/disturbance might have great influence on macro-level characteristics and statistics of turbulence. Besides, the noise-expansion cascade closely connects randomness of micro-level noise/disturbance and macro-level disorder of turbulence, thus revealing an origin of randomness of turbulence. This also highly suggests that unavoidable thermal fluctuations must be considered when simulating turbulence, even if such fluctuations are several orders of magnitudes smaller than other external environmental disturbances. Hopefully, the ``noise-expansion cascade'' as a fundamental property of the NS equations could greatly deepen our understandings about turbulence, and besides is helpful for attacking the fourth millennium problem posed by Clay Mathematics Institute in 2000. △ Less

Submitted 4 April, 2025; v1 submitted 18 October, 2024; originally announced October 2024.

Comments: 22 pages, 8 figures. Accepted by Journal of Fluid Mechanics

Journal ref: J. Fluid Mech. (2025), vol. 1009, A2

Low-cost demonstration of the Zeeman effect: From qualitative observation to quantitative experiments

Authors: Shao-Han Qin, Yu-Han Ma

Abstract: The Zeeman effect, a fundamental quantum phenomenon, demonstrates the interaction between magnetic fields and atomic systems. While precise spectroscopic measurements of this effect have advanced significantly, there remains a lack of simple, visually accessible demonstrations for educational purposes. Here, we present a low-cost experiment that allows for direct visual observation of the Zeeman e… ▽ More The Zeeman effect, a fundamental quantum phenomenon, demonstrates the interaction between magnetic fields and atomic systems. While precise spectroscopic measurements of this effect have advanced significantly, there remains a lack of simple, visually accessible demonstrations for educational purposes. Here, we present a low-cost experiment that allows for direct visual observation of the Zeeman effect. Our setup involves a flame containing sodium (from table salt) placed in front of a sodium vapor lamp. When a magnetic field is applied to the flame, the shadow cast by the flame noticeably lightens, providing a clear, naked-eye demonstration of the Zeeman effect. Furthermore, we conduct two quantitative experiments using this setup, examining the effects of varying magnetic field strength and sodium concentration. This innovative approach not only enriches the experimental demonstration for teaching atomic physics at undergraduate and high school levels but also provides an open platform for students to explore the Zeeman effect through hands-on experience. △ Less

Submitted 27 August, 2024; originally announced August 2024.

Comments: 5 pages, 6 figures, Comments are welcome. This manuscript is intended for submission to American Journal of Physics

Journal ref: Physics and Engineering 36, 21-27 (2026)

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.

Polarizations Underdescribe Vectorial Electromagnetic Waves

Authors: Chunchao Wen, Jianfa Zhang, Chaofan Zhang, Shiqiao Qin, Zhihong Zhu, Wei Liu

Abstract: Electromagnetic waves are described by not only polarization ellipses but also cyclically rotating vectors tracing out them. The corresponding fields are respectively directionless steady line fields and directional instantaneous vector fields. Here we study the seminal topic of electromagnetic scattering from the perspective of instantaneous vector fields and uncover how the global topology of th… ▽ More Electromagnetic waves are described by not only polarization ellipses but also cyclically rotating vectors tracing out them. The corresponding fields are respectively directionless steady line fields and directional instantaneous vector fields. Here we study the seminal topic of electromagnetic scattering from the perspective of instantaneous vector fields and uncover how the global topology of the momentum sphere regulates local distributions of tangent scattered fields. Structurally-stable generic singularities of vector fields move cyclically along lines of linear polarizations and at any instant the index sum of all instantaneous singularities has to be the Euler characteristic $χ=2$. This contrasts sharply with steady line fields, of which generic singularities constrained by the Euler characteristic locate on points of circular polarizations. From such unique perspective of instantaneous singularities, we discovered that for circularly-polarized waves scattered by electromagnetic duality-symmetric particles, since linearly-polarized scatterings are prohibited by helicity conservation, there must exist at least one dark direction along which the scattering is strictly zero. Two such dark directions can be tuned to overlap, along which the scattering would remain zero for arbitrary incident polarizations. We have essentially revealed that \textit{polarizations underdescribe vectorial electromagnetic waves and the instantaneous perspective is indispensable}. The complementarity we discover provides broader and deeper insights into not only electromagnetism, but also other branches of wave physics where singularities are generic and ubiquitous. △ Less

Submitted 15 February, 2025; v1 submitted 10 June, 2024; originally announced June 2024.

Comments: Wei Liu acknowledges many illuminating correspondences with Sir Michael Berry, whose monumental paper with J. F. Nye on phase singularities was published 50 years ago

One nose but two nostrils: Learn to align with sparse connections between two olfactory cortices

Authors: Bo Liu, Shanshan Qin, Venkatesh Murthy, Yuhai Tu

Abstract: The integration of neural representations in the two hemispheres is an important problem in neuroscience. Recent experiments revealed that odor responses in cortical neurons driven by separate stimulation of the two nostrils are highly correlated. This bilateral alignment points to structured inter-hemispheric connections, but detailed mechanism remains unclear. Here, we hypothesized that continuo… ▽ More The integration of neural representations in the two hemispheres is an important problem in neuroscience. Recent experiments revealed that odor responses in cortical neurons driven by separate stimulation of the two nostrils are highly correlated. This bilateral alignment points to structured inter-hemispheric connections, but detailed mechanism remains unclear. Here, we hypothesized that continuous exposure to environmental odors shapes these projections and modeled it as online learning with local Hebbian rule. We found that Hebbian learning with sparse connections achieves bilateral alignment, exhibiting a linear trade-off between speed and accuracy. We identified an inverse scaling relationship between the number of cortical neurons and the inter-hemispheric projection density required for desired alignment accuracy, i.e., more cortical neurons allow sparser inter-hemispheric projections. We next compared the alignment performance of local Hebbian rule and the global stochastic-gradient-descent (SGD) learning for artificial neural networks. We found that although SGD leads to the same alignment accuracy with modestly sparser connectivity, the same inverse scaling relation holds. We showed that their similar performance originates from the fact that the update vectors of the two learning rules align significantly throughout the learning process. This insight may inspire efficient sparse local learning algorithms for more complex problems. △ Less

Submitted 6 May, 2024; originally announced May 2024.

Journal ref: PRX Life 2, 043016 (2024)

Is a direct numerical simulation (DNS) of Navier-Stokes equations with small enough grid spacing and time-step definitely reliable/correct?

Authors: Shejie Qin, Yu Yang, Yongxiang Huang, Xinyu Mei, Lipo Wang, Shijun Liao

Abstract: Traditionally, results given by the direct numerical simulation (DNS) of Navier-Stokes equations are widely regarded as reliable benchmark solutions of turbulence, as long as grid spacing is fine enough (i.e. less than the minimum Kolmogorov scale) and time-step is small enough, say, satisfying the Courant-Friedrichs-Lewy condition. Is this really true? In this paper a two-dimensional sustained tu… ▽ More Traditionally, results given by the direct numerical simulation (DNS) of Navier-Stokes equations are widely regarded as reliable benchmark solutions of turbulence, as long as grid spacing is fine enough (i.e. less than the minimum Kolmogorov scale) and time-step is small enough, say, satisfying the Courant-Friedrichs-Lewy condition. Is this really true? In this paper a two-dimensional sustained turbulent Kolmogorov flow is investigated numerically by the two numerical methods with detailed comparisons: one is the traditional `direct numerical simulation' (DNS), the other is the `clean numerical simulation' (CNS). The results given by DNS are a kind of mixture of the false numerical noise and the true physical solution, which however are mostly at the same order of magnitude due to the butterfly-effect of chaos. On the contrary, the false numerical noise of the results given by CNS is much smaller than the true physical solution of turbulence in a long enough interval of time so that a CNS result is very close to the true physical solution and thus can be used as a benchmark solution. It is found that numerical noise as a kind of artificial tiny disturbances can lead to huge deviations at large scale on the two-dimensional Kolmogorov turbulence, not only quantitatively (even in statistics) but also qualitatively (such as symmetry of flow). Thus, fine enough spatial grid spacing with small enough time-step alone cannot guarantee the validity of the DNS: it is only a necessary condition but not sufficient. This finding might challenge some assumptions in investigation of turbulence. So, DNS results of a few sustained turbulent flows might have huge deviations on both of small and large scales from the true solution of Navier-Stokes equations even in statistics. Hopefully, CNS as a new tool to investigate turbulent flows more accurately than DNS could bring us some new discoveries. △ Less

Submitted 29 April, 2024; v1 submitted 11 April, 2024; originally announced April 2024.

Comments: 27 pages, 18 figures

Journal ref: Journal of Ocean Engineering and Science, 9:293-310 (2024)

Intrinsic polarization conversion and avoided-mode crossing in X-cut lithium niobate microrings

Authors: Zelin Tan, Jianfa Zhang, Zhihong Zhu, Wei Chen, Zhengzheng Shao, Ken Liu, Shiqiao Qin

Abstract: Compared with well-developed free space polarization converters, polarization conversion between TE and TM modes in waveguide is generally considered to be caused by shape birefringence, like curvature, morphology of waveguide cross section and scattering. Here, we reveal a hidden polarization conversion mechanism in X-cut lithium niobate microrings, that is the conversion can be implemented by bi… ▽ More Compared with well-developed free space polarization converters, polarization conversion between TE and TM modes in waveguide is generally considered to be caused by shape birefringence, like curvature, morphology of waveguide cross section and scattering. Here, we reveal a hidden polarization conversion mechanism in X-cut lithium niobate microrings, that is the conversion can be implemented by birefringence of waveguides, which will also introduce an unavoidable avoided-mode crossing. In the experiment, we find that this mode crossing results in severe suppression of one sideband in local nondegenerate four-wave mixing and disrupts the cascaded four-wave mixing on this side. Simultaneously, we proposed, for the first time to our best knowledge, one two-dimensional method to simulate the eigenmodes (TE and TM) in X-cut microrings, which avoids the obstacle from large computational effort in three-dimensional anisotropic microrings simulation, and the mode crossing point. This work will provide an entirely novel approach to the design of polarization converters and simulation for monolithic photonics integrated circuits, and may be helpful to the studies of missed temporal dissipative soliton formation in X-cut lithium niobate rings. △ Less

Submitted 10 March, 2024; originally announced March 2024.

High-fidelity velocity and concentration measurements of turbulent buoyant jets

Authors: Valentina Valori, Sunming Qin, Victor Petrov, Annalisa Manera

Abstract: Accurate models of turbulent buoyant flows are essential for the design of nuclear reactors thermal hydraulics and passive safety systems. However, available models fail to fully capture the physics of turbulent mixing when buoyancy becomes predominant with respect to momentum. Therefore, high-fidelity experiments of well-controlled fundamental flows are needed to develop and validate more accurat… ▽ More Accurate models of turbulent buoyant flows are essential for the design of nuclear reactors thermal hydraulics and passive safety systems. However, available models fail to fully capture the physics of turbulent mixing when buoyancy becomes predominant with respect to momentum. Therefore, high-fidelity experiments of well-controlled fundamental flows are needed to develop and validate more accurate models. We analyze experiments of positive and negative turbulent buoyant jets, both in uniform and stratified environments, with the aim of understanding the thermal hydraulics of turbulent mixing with variable density and providing high-fidelity data for the development and validation of turbulence models. Non-intrusive, simultaneous Particle Image Velocimetry and Laser Induced Fluorescence measurements were carried out to acquire instantaneous velocity and concentration fields on a vertical section parallel to the axis of a jet in the self-similar region. The Refractive Index Matching method was applied to measure high-resolution buoyant jets with up to 8.6% density difference. These data are free of the typical errors that characterize optical measurements of buoyancy driven flows (e.g., natural and mixed convection) where the refractive index of the fluid is inhomogeneous throughout the measurement domain. Turbulent statistics and entrainment of buoyant jets in uniform and stratified environments are presented. These data are compared with non-buoyant jets in uniform environment, as a reference to investigate the effects of buoyancy and stratification on turbulent mixing. The results will be used for the assessment of current turbulence models and as basis for the development of a new one that captures turbulent mixing. △ Less

Submitted 26 February, 2024; originally announced February 2024.

Magneto-chiral backscatterings by rotationally symmetric nonreciprocal structures

Authors: Chunchao Wen, Jianfa Zhang, Shiqiao Qin, Zhihong Zhu, Wei Liu

Abstract: It was proved that the joint operation of electromagnetic reciprocity and $n$-fold ($n\geq3$) rotational symmetry would secure arbitrary polarization-independent backscattering efficiency [Phys. Rev. B \textbf{103}, 045422 (2021)]. Here we remove the restriction of reciprocity and study the backscatterings of plane waves by rotationally symmetric magneto-optical structures, with collinear incident… ▽ More It was proved that the joint operation of electromagnetic reciprocity and $n$-fold ($n\geq3$) rotational symmetry would secure arbitrary polarization-independent backscattering efficiency [Phys. Rev. B \textbf{103}, 045422 (2021)]. Here we remove the restriction of reciprocity and study the backscatterings of plane waves by rotationally symmetric magneto-optical structures, with collinear incident wavevector, rotational axis and externally applied magnetic field. It is revealed that though nonreciprocity removes the degeneracy of backscattering efficiencies for circularly-polarized incident waves of opposite handedness, the remaining rotational symmetry is sufficient to guarantee that the efficiency is related to the polarization ellipticity only, having nothing to do with the orientations of the polarization ellipses. Moreover, the backscattering efficiency reaches its extremes (maximum or minimum values) always for circularly-polarized incident waves, and for other polarizations the efficiency is their ellipticity-weighted arithmetic average. The principles we have revealed are dictated by rotational symmetries only, which are irrelevant to specific geometric or optical parameters and are intrinsically robust against any rotational-symmetry preserving perturbations. The correlations we have discovered could be further exploited for fundamental explorations in nonreciprocal photonics and practical applications including polarimetry and ellipsometry. △ Less

Submitted 24 December, 2023; originally announced December 2023.

Comments: 6 pages and 4 figures; Comments to welcome

Spatio-Temporal Nonlinear Theory in Birefringent Microrings

Authors: Zelin Tan, Xingqiao Chen, Ning Liu, Jipeng Xu, Jianfa Zhang, Zhihong Zhu, Ken Liu, Shiqiao Qin

Abstract: Frequency-dependent nonlinear process in microresonators is widely acknowledged, but there is no theory available to calculate the conversion efficiency for each resonance of the ring, except for the phase-matching one. Similarly for azimuth-dependent nonlinear process in birefringent rings, there is a lack of theory to analysis the conversion efficiency for each azimuth of the ring. Consequently,… ▽ More Frequency-dependent nonlinear process in microresonators is widely acknowledged, but there is no theory available to calculate the conversion efficiency for each resonance of the ring, except for the phase-matching one. Similarly for azimuth-dependent nonlinear process in birefringent rings, there is a lack of theory to analysis the conversion efficiency for each azimuth of the ring. Consequently, it leads to old-fashioned or ill-considered coupling position and inefficient energy conversion in birefringent microrings. Here, we introduce spatio-temporal coupled-mode equation to describe mode spatial properties in the cavity, compensating for the deficiency of temporal coupled-mode equation in describing sophisticated responses. By this equation, we find that over a wide frequency range, the extremely strong second-harmonic generation can be achieved at different azimuth under different pumps in an X-cut lithium niobate microring, which is important for realizing an efficient entangled quantum light source, for example. This work will provide new ideas and guidelines for design and applications of monolithic birefringent photonic integrated circuits with high efficiency. △ Less

Submitted 19 December, 2023; v1 submitted 29 November, 2023; originally announced November 2023.

Fourier neural operator for real-time simulation of 3D dynamic urban microclimate

Authors: Wenhui Peng, Shaoxiang Qin, Senwen Yang, Jianchun Wang, Xue Liu, Liangzhu Leon Wang

Abstract: Global urbanization has underscored the significance of urban microclimates for human comfort, health, and building/urban energy efficiency. They profoundly influence building design and urban planning as major environmental impacts. Understanding local microclimates is essential for cities to prepare for climate change and effectively implement resilience measures. However, analyzing urban microc… ▽ More Global urbanization has underscored the significance of urban microclimates for human comfort, health, and building/urban energy efficiency. They profoundly influence building design and urban planning as major environmental impacts. Understanding local microclimates is essential for cities to prepare for climate change and effectively implement resilience measures. However, analyzing urban microclimates requires considering a complex array of outdoor parameters within computational domains at the city scale over a longer period than indoors. As a result, numerical methods like Computational Fluid Dynamics (CFD) become computationally expensive when evaluating the impact of urban microclimates. The rise of deep learning techniques has opened new opportunities for accelerating the modeling of complex non-linear interactions and system dynamics. Recently, the Fourier Neural Operator (FNO) has been shown to be very promising in accelerating solving the Partial Differential Equations (PDEs) and modeling fluid dynamic systems. In this work, we apply the FNO network for real-time three-dimensional (3D) urban wind field simulation. The training and testing data are generated from CFD simulation of the urban area, based on the semi-Lagrangian approach and fractional stepping method to simulate urban microclimate features for modeling large-scale urban problems. Numerical experiments show that the FNO model can accurately reconstruct the instantaneous spatial velocity field. We further evaluate the trained FNO model on unseen data with different wind directions, and the results show that the FNO model can generalize well on different wind directions. More importantly, the FNO approach can make predictions within milliseconds on the graphics processing unit, making real-time simulation of 3D dynamic urban microclimate possible. △ Less

Submitted 30 September, 2023; v1 submitted 7 August, 2023; originally announced August 2023.

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

Authors: Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, Prasanna Balaprakash, Victor M. Zavala

Abstract: Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertaint… ▽ More Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making. △ Less

Submitted 28 June, 2024; v1 submitted 19 July, 2023; originally announced July 2023.

Momentum-space Scattering Extremizations

Authors: Chunchao Wen, Jianfa Zhang, Shiqiao Qin, Zhihong Zhu, Wei Liu

Abstract: Studies into scatterings of photonic structures have been so far overwhelmingly focused on their dependencies on the spatial and spectral morphologies of the incident waves. In contrast, the evolution of scattering properties through another parameter space of incident directions (momentum space) has attracted comparably little attention, though of profound importance for various scattering-relate… ▽ More Studies into scatterings of photonic structures have been so far overwhelmingly focused on their dependencies on the spatial and spectral morphologies of the incident waves. In contrast, the evolution of scattering properties through another parameter space of incident directions (momentum space) has attracted comparably little attention, though of profound importance for various scattering-related applications. Here we investigate, from the perspective of quasi-normal modes (QNMs), the momentum-space scattering extremizations with respect to varying incident directions of plane waves. It is revealed that for effective single-QNM excitations, scatterings are maximized exactly along those directions where the QNM radiation reaches its maximum, with matched incident and radiation polarizations. For an arbitrary direction, when the incident polarization is tuned to be orthogonal to that of the mode radiation, the QNM cannot be excited and thus the scatterer becomes invisible with null scatterings. The principles we have revealed are protected by fundamental laws of reciprocity and energy conservation (optical theorem), which can be further expanded and applied for other branches of wave physics. △ Less

Submitted 18 May, 2023; originally announced May 2023.

Comments: 5 pages, 3 figures, and comments to welcome

Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction

Authors: Ursula Ludacka, Jiali He, Shuyu Qin, Manuel Zahn, Emil Frang Christiansen, Kasper A. Hunnestad, Zewu Yan, Edith Bourret, István Kézsmárki, Antonius T. J. van Helvoort, Joshua Agar, Dennis Meier

Abstract: Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the nanoscale. In electronic and quantum materials, this new capability gives access to, for example, emergent chiral structures and symmetry-breaking distortions that underpin functional properties. Quantifying nanoscale structural features with statistical significanc… ▽ More Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the nanoscale. In electronic and quantum materials, this new capability gives access to, for example, emergent chiral structures and symmetry-breaking distortions that underpin functional properties. Quantifying nanoscale structural features with statistical significance, however, is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms, which often results in low signal-to-noise and the superposition of multiple signals that are challenging to deconvolute. Here we apply scanning electron diffraction to explore local polar distortions in the uniaxial ferroelectric Er(Mn,Ti)O$_3$. Using a custom-designed convolutional autoencoder with bespoke regularization, we demonstrate that subtle variations in the scattering signatures of ferroelectric domains, domain walls, and vortex textures can readily be disentangled with statistical significance and separated from extrinsic contributions due to, e.g., variations in specimen thickness or bending. The work demonstrates a pathway to quantitatively measure symmetry-breaking distortions across large areas, mapping structural changes at interfaces and topological structures with nanoscale spatial resolution. △ Less

Submitted 9 May, 2023; originally announced May 2023.

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)

A Self-Adaptive Algorithm of the Clean Numerical Simulation (CNS) for Chaos

Authors: Shijie Qin, Shijun Liao

Abstract: The background numerical noise $\varepsilon_{0} $ is determined by the maximum of truncation error and round-off error. For a chaotic system, the numerical error $\varepsilon(t)$ grows exponentially, say, $\varepsilon(t) = \varepsilon_{0} \exp(κ\,t)$, where $κ>0$ is the so-called noise-growing exponent. This is the reason why one can not gain a convergent simulation of chaotic systems in a long en… ▽ More The background numerical noise $\varepsilon_{0} $ is determined by the maximum of truncation error and round-off error. For a chaotic system, the numerical error $\varepsilon(t)$ grows exponentially, say, $\varepsilon(t) = \varepsilon_{0} \exp(κ\,t)$, where $κ>0$ is the so-called noise-growing exponent. This is the reason why one can not gain a convergent simulation of chaotic systems in a long enough interval of time by means of traditional algorithms in double precision, since the background numerical noise $\varepsilon_{0}$ might stop decreasing because of the use of double precision. This restriction can be overcome by means of the clean numerical simulation (CNS), which can decrease the background numerical noise $\varepsilon_{0}$ to any required tiny level. A lot of successful applications show the novelty and validity of the CNS. In this paper, we further propose some strategies to greatly increase the computational efficiency of the CNS algorithms for chaotic dynamical systems. It is highly suggested to keep a balance between truncation error and round-off error and besides to progressively enlarge the background numerical noise $\varepsilon_{0}$, since the exponentially increasing numerical noise $\varepsilon(t)$ is much larger than it. Some examples are given to illustrate the validity of our strategies for the CNS. △ Less

Submitted 31 January, 2023; originally announced February 2023.

Comments: 18 pages, 3 figures, 3 tables

Journal ref: Adv. Appl. Math. Mech., 15 (2023), pp. 1191-1215

RANS Simulations of Turbulent Round Jets in the Presence of Density Difference and Comparison with High-Resolution Experimental Data

Authors: Jiaxin Mao, Sunming Qin, Victor Petrov, Annalisa Manera

Abstract: In this paper, the novel experimental data reported by Qin et al. [1] are used to assess the predictive capability of the Realizable k-epsilon (RKE) model and Reynolds stress transport (RST) model for buoyant jets and understand the reasons for discrepancies. In particular, we present the comparison between simulation results of a turbulent buoyant jet flow in the self-similar region with high-res… ▽ More In this paper, the novel experimental data reported by Qin et al. [1] are used to assess the predictive capability of the Realizable k-epsilon (RKE) model and Reynolds stress transport (RST) model for buoyant jets and understand the reasons for discrepancies. In particular, we present the comparison between simulation results of a turbulent buoyant jet flow in the self-similar region with high-resolution experimental data obtained for a jet injected from a 2 mm nozzle into a 300x300x300 $mm^3$ tank, with nominal Reynolds number equal to 10,000. Results show that streamwise velocity profiles predicted by the RST model had good agreement with experimental data, while the larger spreading rate was predicted by the RKE model. For turbulent statistics, turbulent kinetic energy witnessed a discrepancy in the center region, with shear stress well predicted for both models. Comparison of the turbulent kinetic energy production term with experimental data revealed reasons for the discrepancy and also showed that the gradient of the streamwise velocity in the crosswise direction contributes the most to the turbulent kinetic energy production. Investigation of model coefficients of the turbulent dissipation equation for the RKE model has revealed that $C_{\varepsilon2}$ is critical in model accuracy. △ Less

Submitted 14 January, 2023; originally announced January 2023.

Journal ref: Proceedings of 18th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-18), 2019

Large-scale influence of numerical noises as artificial stochastic disturbances on a sustained turbulence

Authors: Shijie Qin, Shijun Liao

Abstract: We investigate the large-scale influence of numerical noises as tiny artificial stochastic disturbances on a sustained turbulence. Using the two-dimensional (2D) turbulent Rayleigh-Bénard (RB) convection as an example, we numerically solve the NS equations, separately, by means of a traditional algorithm with double precision (marked by RKwD) and the so-called clean numerical simulation (CNS). The… ▽ More We investigate the large-scale influence of numerical noises as tiny artificial stochastic disturbances on a sustained turbulence. Using the two-dimensional (2D) turbulent Rayleigh-Bénard (RB) convection as an example, we numerically solve the NS equations, separately, by means of a traditional algorithm with double precision (marked by RKwD) and the so-called clean numerical simulation (CNS). The numerical simulation given by the RKwD is a mixture of the "true" physical solution and the "false" numerical noises that is random and can be regarded as a kind of artificial stochastic disturbances: unfortunately, the "true" physical solution is mostly at the same level as the "false" numerical noises. By contrast, the CNS can greatly reduce the background numerical noise to any a required level so that the "false" numerical noises are negligible compared with the "true" physical solution and thus the CNS solution can be used as a "clean" benchmark solution for comparison. It is found that the numerical noises as tiny artificial stochastic disturbances could indeed lead to large-scale deviations of simulations not only in spatio-temporal trajectories but also even in statistics. Especially, these numerical noises (as artificial stochastic disturbances) even lead to different types of flows: the shearing convection occurs for the RKwD simulations, and its corresponding flow field turns to a kind of zonal flow thereafter, however the CNS benchmark solution always sustains the non-shearing vortical/roll-like convection during the whole process of simulation. Thus, we provide a rigorous evidence that numerical noises as a kind of small-scale artificial stochastic disturbances have quantitatively and qualitatively large-scale influences on a sustained turbulence, i.e. the 2D turbulent RB convection considered in this paper. △ Less

Submitted 19 August, 2022; originally announced August 2022.

Comments: 25 pages, 11 figures and 1 table. Accepted by Journal of Fluid Mechanics

Journal ref: Journal of Fluid Mechanics, vol. 948, A7 (2022)

RAR-PINN algorithm for the data-driven vector-soliton solutions and parameter discovery of coupled nonlinear equations

Authors: Shu-Mei Qin, Min Li, Tao Xu, Shao-Qun Dong

Abstract: This work aims to provide an effective deep learning framework to predict the vector-soliton solutions of the coupled nonlinear equations and their interactions. The method we propose here is a physics-informed neural network (PINN) combining with the residual-based adaptive refinement (RAR-PINN) algorithm. Different from the traditional PINN algorithm which takes points randomly, the RAR-PINN alg… ▽ More This work aims to provide an effective deep learning framework to predict the vector-soliton solutions of the coupled nonlinear equations and their interactions. The method we propose here is a physics-informed neural network (PINN) combining with the residual-based adaptive refinement (RAR-PINN) algorithm. Different from the traditional PINN algorithm which takes points randomly, the RAR-PINN algorithm uses an adaptive point-fetching approach to improve the training efficiency for the solutions with steep gradients. A series of experiment comparisons between the RAR-PINN and traditional PINN algorithms are implemented to a coupled generalized nonlinear Schrödinger (CGNLS) equation as an example. The results indicate that the RAR-PINN algorithm has faster convergence rate and better approximation ability, especially in modeling the shape-changing vector-soliton interactions in the coupled systems. Finally, the RAR-PINN method is applied to perform the data-driven discovery of the CGNLS equation, which shows the dispersion and nonlinear coefficients can be well approximated. △ Less

Submitted 29 April, 2022; originally announced May 2022.

Strong Neel ordering and luminescence correlation in a two-dimensional antiferromagnet

Authors: Yongheng Zhou, Kaiyue He, Huamin Hu, Gang Ouyang, Chao Zhu, Wei Wang, Sichen Qin, Ye Tao, Runfeng Chen, Le Zhang, Run Shi, Chun Cheng, Han Wang, Yanjun Liu, Zheng Liu, Taihong Wang, Wei Huang, Lin Wang, Xiaolong Chen

Abstract: Magneto-optical effect has been widely used in light modulation, optical sensing and information storage. Recently discovered two-dimensional (2D) van der Waals layered magnets are considered as promising platforms for investigating novel magneto-optical phenomena and devices, due to the long-range magnetic ordering down to atomically-thin thickness, rich species and tunable properties. However, m… ▽ More Magneto-optical effect has been widely used in light modulation, optical sensing and information storage. Recently discovered two-dimensional (2D) van der Waals layered magnets are considered as promising platforms for investigating novel magneto-optical phenomena and devices, due to the long-range magnetic ordering down to atomically-thin thickness, rich species and tunable properties. However, majority 2D antiferromagnets suffer from low luminescence efficiency which hinders their magneto-optical investigations and applications. Here, we uncover strong light-magnetic ordering interactions in 2D antiferromagnetic MnPS3 utilizing a newly-emerged near-infrared photoluminescence (PL) mode far below its intrinsic bandgap. This ingap PL mode shows strong correlation with the Neel ordering and persists down to monolayer thickness. Combining the DFT, STEM and XPS, we illustrate the origin of the PL mode and its correlation with Neel ordering, which can be attributed to the oxygen ion-mediated states. Moreover, the PL strength can be further tuned and enhanced using ultraviolet-ozone treatment. Our studies offer an effective approach to investigate light-magnetic ordering interactions in 2D antiferromagnetic semiconductors. △ Less

Submitted 6 May, 2022; originally announced May 2022.

Journal ref: Lasers & Photonic Reviews, 2022

A kind of Lagrangian chaotic property of the Arnold-Beltrami-Childress flow

Authors: Shijie Qin, Shijun Liao

Abstract: Three-dimensional steady-state Arnold-Beltrami-Childress (ABC) flow has a chaotic Lagrangian structure, and also satisfies the Navier-Stokes (NS) equations with an external force per unit mass. It is well-known that, although trajectories of a chaotic system have sensitive dependence on initial conditions, i.e. the famous ``butterfly-effect'', their statistical properties are often insensitive to… ▽ More Three-dimensional steady-state Arnold-Beltrami-Childress (ABC) flow has a chaotic Lagrangian structure, and also satisfies the Navier-Stokes (NS) equations with an external force per unit mass. It is well-known that, although trajectories of a chaotic system have sensitive dependence on initial conditions, i.e. the famous ``butterfly-effect'', their statistical properties are often insensitive to small disturbances. This kind of chaos (such as governed by the Lorenz equations) is called normal-chaos. However, a new concept, i.e. ultra-chaos, has been reported recently, whose statistics are unstable to tiny disturbances. Thus, ultra-chaos represents higher disorder than normal chaos. In this paper, we illustrate that ultra-chaos widely exists in Lagrangian trajectories of fluid particles in steady-state ABC flow. Moreover, solving the NS equation when $Re=50$ with the ABC flow plus a very small disturbance as the initial condition, it is found that trajectories of nearly all fluid particles become ultra-chaotic when the transition from laminar to turbulence occurs. These numerical experiments and facts highly suggest that ultra-chaos should have a relationship with turbulence. This paper identifies differences between ultra-chaos and sensitivity of statistics to parameters. Possible relationships between ultra-chaos and the Poincaré section, ultra-chaos and ergodicity/non-ergodicity, etc., are discussed. The concept of ultra-chaos opens a new perspective of chaos, the Poincaré section, ergodicity/non-ergodicity, turbulence and their inter-relationships. △ Less

Submitted 13 May, 2023; v1 submitted 28 April, 2022; originally announced April 2022.

Comments: 23 pages, 11 figures, 7 tables

Journal ref: Journal of Fluid Mechanics, vol. 960, A15, 2023

Improved Gradual Resistive Switching Range and 1000x On/Off Ratio in HfOx RRAM Achieved with a $Ge_2Sb_2Te_5$ Thermal Barrier

Authors: Raisul Islam, Shengjun Qin, Sanchit Deshmukh, Zhouchangwan Yu, Cagil Koroglu, Asir Intisar Khan, Kirstin Schauble, Krishna C. Saraswat, Eric Pop, H. -S. Philip Wong

Abstract: Gradual switching between multiple resistance levels is desirable for analog in-memory computing using resistive random-access memory (RRAM). However, the filamentary switching of $HfO_x$-based conventional RRAM often yields only two stable memory states instead of gradual switching between multiple resistance states. Here, we demonstrate that a thermal barrier of $Ge_2Sb_2Te_5$ (GST) between… ▽ More Gradual switching between multiple resistance levels is desirable for analog in-memory computing using resistive random-access memory (RRAM). However, the filamentary switching of $HfO_x$-based conventional RRAM often yields only two stable memory states instead of gradual switching between multiple resistance states. Here, we demonstrate that a thermal barrier of $Ge_2Sb_2Te_5$ (GST) between $HfO_x$ and the bottom electrode (TiN) enables wider and weaker filaments, by promoting heat spreading laterally inside the $HfO_x$. Scanning thermal microscopy suggests that $HfO_x+GST$ devices have a wider heating region than control devices with only $HfO_x$, indicating the formation of a wider filament. Such wider filaments can have multiple stable conduction paths, resulting in a memory device with more gradual and linear switching. The thermally-enhanced $HfO_x+GST$ devices also have higher on/off ratio ($>10^3$) than control devices ($<10^2$), and a median set voltage lower by approximately 1 V (~35%), with a corresponding reduction of the switching power. Our $HfO_x+GST$ RRAM shows 2x gradual switching range using fast (~ns) identical pulse trains with amplitude less than 2 V. △ Less

Submitted 23 March, 2022; originally announced March 2022.

Ultra-chaos: an insurmountable objective obstacle of reproducibility and replicability

Authors: Shijun Liao, Shijie Qin

Abstract: In this paper, a new concept, i.e. ultra-chaos, is proposed for the first time. Unlike a normal-chaos, statistical properties such as the probability density functions (PDF) of an ultra-chaos are sensitive to tiny disturbances. We illustrate that ultra-chaos is widely existed and thus has general scientific meanings. It is found that statistical non-reproducibility is an inherent property of an ul… ▽ More In this paper, a new concept, i.e. ultra-chaos, is proposed for the first time. Unlike a normal-chaos, statistical properties such as the probability density functions (PDF) of an ultra-chaos are sensitive to tiny disturbances. We illustrate that ultra-chaos is widely existed and thus has general scientific meanings. It is found that statistical non-reproducibility is an inherent property of an ultra-chaos so that an ultra-chaos is at a higher-level of disorder than a normal-chaos. Thus, it is impossible in practice to replicate experimental/numerical results of an ultra-chaos even in statistical meanings, since random environmental noises always exist and are out of control. Thus, the ultra-chaos should be an insurmountable obstacle of reproducibility and replicability. Similar to Gödel's incompleteness theorem, such kind of "incompleteness of reproducibility" reveals a limitation of our traditional scientific paradigm based on reproducible experiments, which can be traced back to Galileo. The ultra-chaos opens a new door and possibility to study chaos theory, turbulence theory, computational fluid dynamics (CFD), the statistical significance, reproducibility crisis, and so on. △ Less

Submitted 19 December, 2021; v1 submitted 30 September, 2021; originally announced October 2021.

Comments: 17 pages, 10 figures. Accepted for publication by Advances in Applied Mathematics and Mechanics in Dec. 2021

Journal ref: Advances in Applied Mathematics and Mechanics, Vol. 14, No. 4, pp. 799-815 (2022)

Influence of database noises to machine learning for spatiotemporal chaos

Authors: Yu Yang, Shijie Qin, Shijun Liao

Abstract: A new strategy, namely the "clean numerical simulation" (CNS), was proposed (J. Computational Physics, 418:109629, 2020) to gain reliable/convergent simulations (with negligible numerical noises) of spatiotemporal chaotic systems in a long enough interval of time, which provide us benchmark solution for comparison. Here we illustrate that machine learning (ML) can always give good enough fitting p… ▽ More A new strategy, namely the "clean numerical simulation" (CNS), was proposed (J. Computational Physics, 418:109629, 2020) to gain reliable/convergent simulations (with negligible numerical noises) of spatiotemporal chaotic systems in a long enough interval of time, which provide us benchmark solution for comparison. Here we illustrate that machine learning (ML) can always give good enough fitting predictions of a spatiotemporal chaos by using, separately, two quite different training sets: one is the "clean database" given by the CNS with negligible numerical noises, the other is the "polluted database" given by the traditional algorithms in single/double precision with considerably large numerical noises. However, even in statistics, the ML predictions based on the "polluted database" are quite different from those based on the "clean database". It illustrates that the database noises have huge influences on ML predictions of some spatiotemporal chaos, even in statistics. Thus, we must use a "clean" database for machine learning of some spatiotemporal chaos. This surprising result might open a new door and possibility to study machine learning. △ Less

Submitted 13 September, 2021; v1 submitted 8 September, 2021; originally announced September 2021.

Comments: 6 figures, 5 pages

Single pixel wide gamut dynamic color modulation based on graphene micromechanical system

Authors: Yanli Xu, Hongxu Li, Xin Zhang, Wenjing Liu, Zhengping Zhang, Shuijie Qin, Jiangtao Liu

Abstract: Dynamic color modulation in the composite structure of graphene microelectromechanical systems (MEMS)- photonic crystal microcavity is investigated in this work. The designed photonic crystal microcavity has three resonant standing wave modes corresponding to the three primary colors of red (R), green (G) and blue (B), forming strong localization of light in three modes at different positions of t… ▽ More Dynamic color modulation in the composite structure of graphene microelectromechanical systems (MEMS)- photonic crystal microcavity is investigated in this work. The designed photonic crystal microcavity has three resonant standing wave modes corresponding to the three primary colors of red (R), green (G) and blue (B), forming strong localization of light in three modes at different positions of the microcavity. Once graphene is added, it can govern the transmittance of three modes. When graphene is located in the abdomen of the standing wave, which has strong light absorption and therefore the structure's transmittance is lower, or when graphene is located in the node of the standing wave, it has weak light absorption and therefore the structure's transmittance is higher. Therefore, the graphene absorption of different colors of light can be regulated dynamically by applying voltages to tune the equilibrium position of the graphene MEMS in the microcavity, consequently realizing the output of vivid monochromatic light or multiple mixed colors of light within a single pixel, thus greatly improving the resolution. Our work provides a route to dynamic color modulation with graphene and provides guidance for the design and manufacture of ultrahigh resolution, ultrafast modulation and wide color gamut interferometric modulator displays. △ Less

Submitted 21 June, 2021; originally announced June 2021.

An application of data driven reward of deep reinforcement learning by dynamic mode decomposition in active flow control

Authors: Sheng Qin, Shuyue Wang, Jean Rabault, Gang Sun

Abstract: This paper focuses on the active flow control (AFC) of the flow over a circular cylinder with synthetic jets through deep reinforcement learning (DRL) by implementing a reward function based on dynamic mode decomposition (DMD). As a main factor that affects the DRL model, the reward is determined by the information extracted from flow field by performing DMD on measurements through simulation. Wit… ▽ More This paper focuses on the active flow control (AFC) of the flow over a circular cylinder with synthetic jets through deep reinforcement learning (DRL) by implementing a reward function based on dynamic mode decomposition (DMD). As a main factor that affects the DRL model, the reward is determined by the information extracted from flow field by performing DMD on measurements through simulation. With the data-driven reward, the DRL model is able to learn the AFC policy through the more global information of the field, and instructs the mass flow rate of the synthetic jets. As a result of this type of AFC, the vortex street is stabilized with a reduction of approximately 8% in drag and an improvement of approximately 109% in recirculation area. Furthermore, the configuration of the flow modified by the AFC is studied with DMD on the velocity measurement of the complete flow field. △ Less

Submitted 7 August, 2021; v1 submitted 11 June, 2021; originally announced June 2021.

Comments: arXiv admin note: text overlap with arXiv:1808.07664 by other authors

The short-term seasonal analyses between atmospheric environment and COVID-19 in epidemic areas of Cities in Australia, South Korea, and Italy

Authors: Yuxi Liu, Xin Lin, Shaowen Qin

Abstract: The impact of the outbreak of COVID-19 on health has been widely concerned. Disease risk assessment, prediction, and early warning have become a significant research field. Previous research suggests that there is a relationship between air quality and the disease. This paper investigated the impact of the atmospheric environment on the basic reproduction number (R$_0$) in Australia, South Korea,… ▽ More The impact of the outbreak of COVID-19 on health has been widely concerned. Disease risk assessment, prediction, and early warning have become a significant research field. Previous research suggests that there is a relationship between air quality and the disease. This paper investigated the impact of the atmospheric environment on the basic reproduction number (R$_0$) in Australia, South Korea, and Italy by using atmospheric environment data, confirmed case data, and the distributed lag non-linear model (DLNM) model based on Quasi-Poisson regression. The results show that the air temperature and humidity have lag and persistence on short-term R$_0$, and seasonal factors have an apparent decorating effect on R$_0$. PM$_{10}$ is the primary pollutant that affects the excess morbidity rate. Moreover, O$_3$, PM$_{2.5}$, and SO$_2$ as perturbation factors have an apparent cumulative effect. These results present beneficial knowledge for correlation between environment and COVID-19, which guiding prospective analyses of disease data. △ Less

Submitted 23 May, 2020; originally announced May 2020.