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Paper Publications

[10]Xiaoli Chen, Fengyan Wu*, Jinqiao Duan, Jürgen Kurths, Xiaofan Li. Most probable dynamics of a genetic regulatory network under stable Lévy noise, Applied Mathematics and Computation, 348: 425-436 (2019).
[9]Xiaoli Chen, Jinqiao Duan*, Nonlocal dynamics for non-Gaussiansystems arising in biophysical modeling, Communications on Applied Mathematics and Computation, 2(2): 201-213 (2020).
[8]Jian Ren, Xiaoli Chen*. Identifying stochastic governing equations from data of the most probable transition trajectories. Stochastics and Dynamics, 2340010 (2023).
[7]Xiaoli Chen, Hui Wang*, Jinqiao Duan, Detecting stochastic governing laws with observation on stationary distributions. Physica D: Nonlinear Phenomena, 448, 133691 (2023).
[6]Xiaoli Chen*, Jinqiao Duan, George Em Karniadakis, Learning and Meta-Learning of stochastic Advection-Diffusion-Reaction systems from sparse measurements, European Journal of Applied Mathematics, 32(3):397-420 (2021).
[5]Xiaoge Guo, Xiaoli Chen, Yan Li,Xiuting Li. Data-driven discovery of slow–fast stochastic differential equations with data binning and sparse bayesian strategy. Stochastics and Dynamics, 2550020 (2025).
[4]Jiangyan Liu , jiaqian Zhao, Ming Yi, Jinqiao Duan, Xiaoli Chen. Data driven discovery of escape phenomena in stochastic systems. Chaos, 35(5) ( 2025).
[3]Xiaoli Chen*, Liu Yang, Jinqiao Duan, George Em Karniadakis, Solving inverse stochastic problems from discrete particle observations using the Fokker-Planck equation and physics-informed neural networks, SIAM Journal on Scientific Computing, 43(3): B811-B830 (2021).（高被引和热点论文）
[2]Xiaoli Chen, Phoebus Rosakis, Zhizhang Wu*, and Zhiwen Zhang*. Solving nonconvex energy minimization problems in martensitic phase transitions with a mesh-free deep learning approach. Computer Methods in Applied Mechanics and Engineering 416: 116384 (2023).（数学与跨学科应用顶刊、中国数学会分区：T1）
[1]Xiaoli Chen#, Beatrice W. Soh#, Zi-En Ooi, Eleonore Vissol-Gaudin, Haijun Yu, Kostya S. Novoselov*, Kedar Hippalgaonkar*, and Qianxiao Li*. Constructing custom thermodynamics using deep learning. Nature Computational Science, 4(1): 66-85 (2024).（Nature子刊）

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