Showing 1–17 of 17 results for author: Qin, T

Saturation-Aware Robust Trajectory Optimization for Reusable Launch Vehicles via Differentiable Physics

Authors: Liwei Chen, Tong Qin

Abstract: The high-angle-of-attack flip maneuver of reusable launch vehicles presents significant challenges for robust trajectory optimization due to the combined effects of highly nonlinear dynamics, aerodynamic uncertainties, and actuator saturation. This paper presents a differentiable physics framework for saturation-aware robust trajectory optimization. At its core, a Differentiable Particle Tube Cont… ▽ More The high-angle-of-attack flip maneuver of reusable launch vehicles presents significant challenges for robust trajectory optimization due to the combined effects of highly nonlinear dynamics, aerodynamic uncertainties, and actuator saturation. This paper presents a differentiable physics framework for saturation-aware robust trajectory optimization. At its core, a Differentiable Particle Tube Control (DPTC) scheme is developed to optimize uncertainty evolution through an ensemble-based distribution shaping strategy. State uncertainty is represented by a Lagrangian particle ensemble, while hard actuator projection operators are embedded directly into the computational graph, enabling the joint optimization of the nominal feedforward trajectory and a time-varying feedback policy via end-to-end backpropagation. The proposed framework is evaluated against an automatic differentiation-based Successive Convexification (AD-SCvx) baseline combined with a conventional covariance steering feedback strategy. Six-degree-of-freedom Monte Carlo simulations demonstrate that, although the baseline achieves nominal fuel-optimal solutions, its unconstrained feedback formulation becomes susceptible to actuator saturation under aerodynamic disturbances, leading to degraded closed-loop robustness. In contrast, the proposed DPTC framework proactively performs a constraint-aware performance trade-off by relaxing spatial tracking to preserve critical control authority. These results demonstrate that integrating differentiable physics with ensemble-based optimization provides an effective and practical framework for robust guidance in highly constrained aerospace flight systems. △ Less

Submitted 2 July, 2026; originally announced July 2026.

Accelerating Locality-Driven Integration in Quantum Chemistry with Block-Structured Matrix Multiplication

Authors: Xinran Wei, Yan Pan, Fusong Ju, Zehao Zhou, Yihong Zhang, Lin Huang, Jianwei Zhu, Jia Zhang, Huanhuan Xia, Bin Shao, Tao Qin

Abstract: Locality-driven integration is a pervasive computational pattern in quantum chemistry, arising whenever spatially localized basis functions interact through numerical quadrature or integral screening. The dominant matrix multiplications in these tasks exhibit dynamic, structured sparsity driven by spatial locality, posing significant challenges for both dense batched kernels and generic sparse for… ▽ More Locality-driven integration is a pervasive computational pattern in quantum chemistry, arising whenever spatially localized basis functions interact through numerical quadrature or integral screening. The dominant matrix multiplications in these tasks exhibit dynamic, structured sparsity driven by spatial locality, posing significant challenges for both dense batched kernels and generic sparse formats on GPUs. We present KerneLDI, a GPU-oriented framework that addresses this regime by co-designing data layout, screening logic, and matrix-computation operators to realize block-structured matrix multiplication for locality-driven integration. KerneLDI reorganizes operand matrices into a unified block-filtered representation that retains only spatially relevant blocks, and executes the resulting contractions with customized dense block multipliers that adapt proven dense-matmul optimizations to retained block pairs. We develop and evaluate KerneLDI on exchange--correlation (EXC) integration in Kohn--Sham density functional theory, a representative and computationally critical instance of this pattern. Across diverse molecular systems, KerneLDI preserves numerical accuracy while delivering up to 10$\times$ speedup for EXC evaluation over a dense GPU baseline, scales favorably with increasing system size and multi-GPU parallelism, accelerates end-to-end self-consistent field calculations, and yields nearly 6$\times$ throughput improvement for ab initio molecular dynamics. △ Less

Submitted 13 May, 2026; v1 submitted 11 May, 2026; originally announced May 2026.

Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing

Authors: Chu Wang, Lin Huang, Xinran Wei, Tao Qin, Arthur Jiang, Lixue Cheng, Jia Zhang

Abstract: Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However, a fundamental reliance of these models on fixed-cutoff architectures limits their applicability to macromolecular systems where long-range interactions dominate. We demonstrate that this locality constraint causes force… ▽ More Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However, a fundamental reliance of these models on fixed-cutoff architectures limits their applicability to macromolecular systems where long-range interactions dominate. We demonstrate that this locality constraint causes force prediction errors to scale monotonically with system size, revealing a critical architectural bottleneck. To overcome this, we establish the systematically designed MolLR25 ({Mol}ecules with {L}ong-{R}ange effect) benchmark up to 1200 atoms, generated using high-fidelity DFT, and introduce E2Former-LSR, an equivariant transformer that explicitly integrates long-range attention blocks. E2Former-LSR exhibits stable error scaling, achieves superior fidelity in capturing non-covalent decay, and maintains precision on complex protein conformations. Crucially, its efficient design provides up to 30% speedup compared to purely local models. This work validates the necessity of non-local architectures for generalizable MLFFs, enabling high-fidelity molecular dynamics for large-scale chemical and biological systems. △ Less

Submitted 7 January, 2026; originally announced January 2026.

Fast Non-Line-of-Sight Transient Data Simulation and an Open Benchmark Dataset

Authors: Yingjie Shi, Jinye Miao, Taotao Qin, Fuyao Cai, Yi Wei, Lingfeng Liu, Tongyao Li, Chenyang Wu, Huan Liang, Yuyang Yin, Lianfa Bai, Enlai Guo, Jing Han

Abstract: Non-Line-of-Sight (NLOS) imaging reconstructs the shape and depth of hidden objects from picosecond-resolved transient signals, offering potential applications in autonomous driving, security, and medical diagnostics. However, current NLOS experiments rely on expensive hardware and complex system alignment, limiting their scalability. This manuscript presents a simplified simulation method that ge… ▽ More Non-Line-of-Sight (NLOS) imaging reconstructs the shape and depth of hidden objects from picosecond-resolved transient signals, offering potential applications in autonomous driving, security, and medical diagnostics. However, current NLOS experiments rely on expensive hardware and complex system alignment, limiting their scalability. This manuscript presents a simplified simulation method that generates NLOS transient data by modeling light-intensity transport rather than performing conventional path tracing, significantly enhancing computational efficiency. All scene elements, including the relay surface, hidden target, stand-off distance, detector time resolution, and acquisition window are fully parameterized, allowing for rapid configuration of test scenarios. Reconstructions based on the simulated data accurately recover hidden geometries, validating the effectiveness of the approach. The proposed tool reduces the entry barrier for NLOS research and supports the optimization of system design. △ Less

Submitted 4 June, 2025; originally announced June 2025.

UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials

Authors: Gongbo Zhang, Yanting Li, Renqian Luo, Pipi Hu, Yang Yang, Zeru Zhao, Lingbo Li, Guoqing Liu, Zun Wang, Ran Bi, Kaiyuan Gao, Liya Guo, Yu Xie, Chang Liu, Jia Zhang, Tian Xie, Robert Pinsler, Claudio Zeni, Ziheng Lu, Hongxia Hao, Yingce Xia, Marwin Segler, Maik Riechert, Wei Yang, Hao Jiang , et al. (9 additional authors not shown)

Abstract: Function in natural systems arises from one-dimensional sequences forming three-dimensional structures with specific properties. However, current generative models suffer from critical limitations: training objectives seldom target function directly, discrete sequences and continuous coordinates are optimized in isolation, and conformational ensembles are under-modeled. We present UniGenX, a unifi… ▽ More Function in natural systems arises from one-dimensional sequences forming three-dimensional structures with specific properties. However, current generative models suffer from critical limitations: training objectives seldom target function directly, discrete sequences and continuous coordinates are optimized in isolation, and conformational ensembles are under-modeled. We present UniGenX, a unified generative foundation model that addresses these gaps by co-generating sequences and coordinates under direct functional and property objectives across proteins, molecules, and materials. UniGenX represents heterogeneous inputs as a mixed stream of symbolic and numeric tokens, where a decoder-only autoregressive transformer provides global context and a conditional diffusion head generates numeric fields steered by task-specific tokens. Besides the new high SOTAs on structure prediction tasks, the model demonstrates state-of-the-art or competitive performance for the function-aware generation across domains: in materials, it achieves "conflicted" multi-property conditional generation, yielding 436 crystal candidates meeting triple constraints, including 11 with novel compositions; in chemistry, it sets new benchmarks on five property targets and conformer ensemble generation on GEOM; and in biology, it improves success in modeling protein induced fit (RMSD < 2 Å) by over 23-fold and enhances EC-conditioned enzyme design. Ablation studies and cross-domain transfer substantiate the benefits of joint discrete-continuous training, establishing UniGenX as a significant advance from prediction to controllable, function-aware generation. △ Less

Submitted 26 August, 2025; v1 submitted 9 March, 2025; originally announced March 2025.

Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning

Authors: Yuxuan Ren, Dihan Zheng, Chang Liu, Peiran Jin, Yu Shi, Lin Huang, Jiyan He, Shengjie Luo, Tao Qin, Tie-Yan Liu

Abstract: In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm. Nevertheless, data of different molecular properties are often not aligned: some quantities, e.g. equilibrium structure, demand more cost to compute than others, e.g.… ▽ More In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm. Nevertheless, data of different molecular properties are often not aligned: some quantities, e.g. equilibrium structure, demand more cost to compute than others, e.g. energy, so their data are often generated by cheaper computational methods at the cost of lower accuracy, which cannot be directly overcome through multi-task learning. Moreover, it is not straightforward to leverage abundant data of other tasks to benefit a particular task. To handle such data heterogeneity challenges, we exploit the specialty of molecular tasks that there are physical laws connecting them, and design consistency training approaches that allow different tasks to exchange information directly so as to improve one another. Particularly, we demonstrate that the more accurate energy data can improve the accuracy of structure prediction. We also find that consistency training can directly leverage force and off-equilibrium structure data to improve structure prediction, demonstrating a broad capability for integrating heterogeneous data. △ Less

Submitted 13 October, 2024; originally announced October 2024.

Comments: Published as a conference paper at NeurIPS 2024

Bulk photovoltaic effect in two-dimensional ferroelectric semiconductor $α$-In$_2$Se$_3$

Authors: Xiaojuan Chen, Kang Xu, Tingxiao Qin, Yubin Wang, Yuzhong Chen, Haiyun Liu, Qihua Xiong

Abstract: Bulk photovoltaic effect, which arises from crystal symmetry-driven charge carrier separation, is an intriguing physical phenomenon that has attracted extensive interest in photovoltaic application due to its junction-free photovoltaic and potential to surpass Shockley-Queisser limit. Whereas conventional ferroelectric materials mostly suffer from extremely low photocurrent density and weak photov… ▽ More Bulk photovoltaic effect, which arises from crystal symmetry-driven charge carrier separation, is an intriguing physical phenomenon that has attracted extensive interest in photovoltaic application due to its junction-free photovoltaic and potential to surpass Shockley-Queisser limit. Whereas conventional ferroelectric materials mostly suffer from extremely low photocurrent density and weak photovoltaic response at visible light wavelengths. Emerging two-dimensional ferroelectric semiconductors with coupled visible light absorption and spontaneous polarization characteristics are a promising alternative for making functional photoferroelectrics. Herein, we report the experimental demonstration of the bulk photovoltaic effect behavior based on the 2D ferroelectric semiconductor {$α$-InSe caused by an out-of-plane polarization induced depolarization field. The {$α$-InSe device exhibits enhanced bulk photovoltaic response in the visible light spectrum owing to its narrow bandgap. It was demonstrated that the generated photovoltaic current density was nearly two orders of magnitude greater than conventional bulk ferroelectric materials. These findings highlight the potential of 2D ferroelectric semiconductor materials for bulk photovoltaic applications in a broad spectral region. △ Less

Submitted 21 August, 2023; v1 submitted 16 August, 2023; originally announced August 2023.

Large-scale single-photon imaging

Authors: Liheng Bian, Haoze Song, Lintao Peng, Xuyang Chang, Xi Yang, Roarke Horstmeyer, Lin Ye, Tong Qin, Dezhi Zheng, Jun Zhang

Abstract: Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing. However, large-scale high-fidelity single-photon imaging remains a big challenge, due to the complex hardware manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we introduce deep lea… ▽ More Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing. However, large-scale high-fidelity single-photon imaging remains a big challenge, due to the complex hardware manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging over an order of magnitude, with significant enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 $\times$ 32 pixels, 90 scenes, 10 different bit depth, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this real-world physical noise model, we for the first time synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depth, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique on a series of experiments including macroscopic and microscopic imaging, microfluidic inspection, and Fourier ptychography. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance, with more than 5 dB superiority on PSNR compared to the existing methods. △ Less

Submitted 27 December, 2022; originally announced December 2022.

Phonon-induced anomalous gauge potential for photonic isolation in frequency space

Authors: Jianfan Yang, Luqi Yuan, Tian Qin, Fangxing Zhang, Yao Chen, Xiaoshun Jiang, Xianfeng Chen, Shanhui Fan, Wenjie Wan

Abstract: Photonic gauge potentials are crucial for manipulating charge-neutral photons like their counterpart electrons in the electromagnetic field, allowing analogous Aharonov-Bohm effect in photonics and paving the way for critical applications like photonic isolation. Normally, a gauge potential exhibits phase inversion along two opposite propagation paths. Here we experimentally demonstrate phonon-ind… ▽ More Photonic gauge potentials are crucial for manipulating charge-neutral photons like their counterpart electrons in the electromagnetic field, allowing analogous Aharonov-Bohm effect in photonics and paving the way for critical applications like photonic isolation. Normally, a gauge potential exhibits phase inversion along two opposite propagation paths. Here we experimentally demonstrate phonon-induced anomalous gauge potentials with non-inverted gauge phases in a spatial-frequency space, where quasi-phase-matched nonlinear Brillouin scatterings enable such unique direction-dependent gauge phases. Based on this scheme, we construct photonic isolators in the frequency domain permitting nonreciprocal propagation of light along the frequency axis, where coherent phase control in the photonic isolator allows switching completely the directionality through an Aharonov-Bohm interferometer. Moreover, similar coherent controlled unidirectional frequency conversions are also illustrated. These results may offer a unique platform for a compact, integrated solution to implement synthetic-dimension devices for on-chip optical signal processing. △ Less

Submitted 7 August, 2020; originally announced August 2020.

Data-driven learning of non-autonomous systems

Authors: Tong Qin, Zhen Chen, John Jakeman, Dongbin Xiu

Abstract: We present a numerical framework for recovering unknown non-autonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper mode… ▽ More We present a numerical framework for recovering unknown non-autonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method. △ Less

Submitted 2 June, 2020; originally announced June 2020.

Floquet and Anomalous Floquet Weyl Semimetals

Authors: Yufei Zhu, Tao Qin, Xinxin Yang, Gao Xianlong, Zhaoxin Liang

Abstract: The periodic driving of a quantum system can enable new topological phases without analogs in static systems. This provides a route towards preparing non-equilibrium quantum phases rooted into the non-equilibrium nature by periodic driving engineering. Motivated by the ongoing considerable interest in topological semimetals, we are interested in the novel topological phases in the periodically dri… ▽ More The periodic driving of a quantum system can enable new topological phases without analogs in static systems. This provides a route towards preparing non-equilibrium quantum phases rooted into the non-equilibrium nature by periodic driving engineering. Motivated by the ongoing considerable interest in topological semimetals, we are interested in the novel topological phases in the periodically driven topological semimetals without a static counterpart. We propose to design non-equilibrium topological semimetals in the regime of weakly driving field where the spectrum width of shares the same magnitude with the driving frequency. We identify two novel types of non-equilibrium Weyl semimetals (i.e., Floquet and anomalous Floquet Weyl semimetals) that do not exhibit analogues in equilibrium. The proposed setup is shown to be experimentally feasible using the state-of-the-art techniques used to control ultracold atoms in optical lattices. △ Less

Submitted 15 July, 2020; v1 submitted 12 March, 2020; originally announced March 2020.

Journal ref: Phys. Rev. Research 2, 033045 (2020)

Deep learning of parameterized equations with applications to uncertainty quantification

Authors: Tong Qin, Zhen Chen, John Jakeman, Dongbin Xiu

Abstract: We propose a numerical method for discovering unknown parameterized dynamical systems by using observational data of the state variables. Our method is built upon and extends the recent work of discovering unknown dynamical systems, in particular those using deep neural network (DNN). We propose a DNN structure, largely based upon the residual network (ResNet), to not only learn the unknown form o… ▽ More We propose a numerical method for discovering unknown parameterized dynamical systems by using observational data of the state variables. Our method is built upon and extends the recent work of discovering unknown dynamical systems, in particular those using deep neural network (DNN). We propose a DNN structure, largely based upon the residual network (ResNet), to not only learn the unknown form of the governing equation but also take into account the random effect embedded in the system, which is generated by the random parameters. Once the DNN model is successfully constructed, it is able to produce system prediction over longer term and for arbitrary parameter values. For uncertainty quantification, it allows us to conduct uncertainty analysis by evaluating solution statistics over the parameter space. △ Less

Submitted 10 March, 2020; v1 submitted 15 October, 2019; originally announced October 2019.

Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data

Authors: Kailiang Wu, Tong Qin, Dongbin Xiu

Abstract: We present a numerical approach for approximating unknown Hamiltonian systems using observation data. A distinct feature of the proposed method is that it is structure-preserving, in the sense that it enforces conservation of the reconstructed Hamiltonian. This is achieved by directly approximating the underlying unknown Hamiltonian, rather than the right-hand-side of the governing equations. We p… ▽ More We present a numerical approach for approximating unknown Hamiltonian systems using observation data. A distinct feature of the proposed method is that it is structure-preserving, in the sense that it enforces conservation of the reconstructed Hamiltonian. This is achieved by directly approximating the underlying unknown Hamiltonian, rather than the right-hand-side of the governing equations. We present the technical details of the proposed algorithm and its error estimate in a special case, along with a practical de-noising procedure to cope with noisy data. A set of numerical examples are then presented to demonstrate the structure-preserving property and effectiveness of the algorithm. △ Less

Submitted 19 August, 2020; v1 submitted 24 May, 2019; originally announced May 2019.

Comments: 27 pages, 19 figures

Journal ref: SIAM Journal on Scientific Computing 42 (6), A3704--A3729, 2020

Slow and fast light enhanced light drag in a moving microcavity

Authors: Tian Qin, Jianfan Yang, Fangxing Zhang, Yao Chen, Dongyi Shen, Wei Liu, Lei Chen, Yuanlin Zheng, Xianfeng Chen, Wenjie Wan

Abstract: Fizeau experiment, inspiring Einstein's special theory of relativity, reveals a small dragging effect of light inside a moving medium. Dispersion can enhance such light drag according to Lorentz's predication. Here we experimentally demonstrate slow and fast light enhanced light drag in a moving optical microcavity through stimulated Brillouin scattering induced transparency and absorption. The st… ▽ More Fizeau experiment, inspiring Einstein's special theory of relativity, reveals a small dragging effect of light inside a moving medium. Dispersion can enhance such light drag according to Lorentz's predication. Here we experimentally demonstrate slow and fast light enhanced light drag in a moving optical microcavity through stimulated Brillouin scattering induced transparency and absorption. The strong dispersion provides an enhancement factor up to ~10^4, greatly reducing the system size down to the micrometer range. These results may offer a unique platform for a compact, integrated solution to motion sensing and ultrafast signal processing applications. △ Less

Submitted 19 May, 2019; originally announced May 2019.

Passive thermal fine-tuning of whispering gallery mode for nonlinear optics in a microcavity

Authors: Yaming Feng, Yuanlin Zheng, Fangxing Zhang, Jianfan Yang, Tian Qin, Wenjie Wan

Abstract: Whispering gallery mode (WGM) microcavities strongly enhance nonlinear optical processes like optical frequency comb, Raman scattering and optomechanics, which nowadays enable cutting-edge applications in microwave synthesis, optical sensing spectroscopy, and integrated photonics. Yet, tunability of their resonances, mostly via coarse and complicated mechanism through temperature, electrical or me… ▽ More Whispering gallery mode (WGM) microcavities strongly enhance nonlinear optical processes like optical frequency comb, Raman scattering and optomechanics, which nowadays enable cutting-edge applications in microwave synthesis, optical sensing spectroscopy, and integrated photonics. Yet, tunability of their resonances, mostly via coarse and complicated mechanism through temperature, electrical or mechanical means, still poses a major challenge for precision applications as above. Here we introduce a new passive scheme to finely tune resonances of WGMs at MHz precision with an external probe. Such probe remotely transfers heat through a gap from an optical microcavity, effectively tuning its resonances by thermal-optic nonlinearity. Moreover, we explore this unique technique in microcavity nonlinear optics, demonstrating the generation of a tunable optical frequency comb and backward stimulated Brillouin scattering with variable beating frequencies. This new technique addresses the core problem of WGM microcavity's fine-tuning, paving the way for important applications like spectroscopy and frequency synthesis. △ Less

Submitted 20 December, 2018; originally announced December 2018.

Comments: 9 pages,4 figures

The effect of phase change on stability of convective flow in a layer of volatile liquid driven by a horizontal temperature gradient

Authors: Roman O. Grigoriev, Tongran Qin

Abstract: Buoyancy-thermocapillary convection in a layer of volatile liquid driven by a horizontal temperature gradient arises in a variety of situations. Recent studies have shown that the composition of the gas phase, which is typically a mixture of vapour and air, has a noticeable effect on the critical Marangoni number describing the onset of convection as well as on the observed convection pattern. Spe… ▽ More Buoyancy-thermocapillary convection in a layer of volatile liquid driven by a horizontal temperature gradient arises in a variety of situations. Recent studies have shown that the composition of the gas phase, which is typically a mixture of vapour and air, has a noticeable effect on the critical Marangoni number describing the onset of convection as well as on the observed convection pattern. Specifically, as the total pressure or, equivalently, the average concentration of air is decreased, the threshold of the instability leading to the emergence of convective rolls is found to increase rather significantly. We present a linear stability analysis of the problem which shows that this trend can be readily understood by considering the transport of heat and vapour through the gas phase. In particular, we show that transport in the gas phase has a noticeable effect even at atmospheric conditions, when phase change is greatly suppressed. △ Less

Submitted 26 September, 2017; v1 submitted 3 April, 2017; originally announced April 2017.

Observation of Gain Spiking and Nonlinear Beating of Optical Frequency Comb in a Microcavity

Authors: Yuanlin Zheng, Tian Qin, Jianfan Yang, Xianfeng Chen, Li Ge, Wenjie Wan

Abstract: Optical frequency combs are crucial for both fundamental science and applications demanding wide frequency spanning and ultra-precision resolutions. Recent advancements of nonlinear Kerr effect based optical frequency combs in microcavities open up new opportunities in a compact platform, however, internal cavity-enhanced nonlinearities are still unclear. Here we demonstrate transient nonlinear dy… ▽ More Optical frequency combs are crucial for both fundamental science and applications demanding wide frequency spanning and ultra-precision resolutions. Recent advancements of nonlinear Kerr effect based optical frequency combs in microcavities open up new opportunities in a compact platform, however, internal cavity-enhanced nonlinearities are still unclear. Here we demonstrate transient nonlinear dynamics during optical frequency comb formation inside a Kerr microcavity. We show that gain spiking forms due to nonlinear phase modulation, causing comb lines' self-detuning nearby a cavity resonance, this introduces one key mechanism to stabilize optical frequency combs. Moreover, nonlinear beating has be observed by injecting an external probe to exam nonlinear cross-phase modulation between comb lines. Nonlinear transient dynamics here reveal the hidden nonlinear features of Kerr based optical frequency combs, leading to a new direction for ultrawide, ultrastable and ultrafast frequency comb generation in microcavities. △ Less

Submitted 31 March, 2017; originally announced March 2017.

Comments: 10 pages, 4 figures

Journal ref: Opt. Express 25, 31140 (2017)