Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks

24 August 2020

Papers citing "Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks"

9 / 9 papers shown

Title
A Robust Model-Based Approach for Continuous-Time Policy Evaluation with Unknown Lévy Process Dynamics Qihao Ye Xiaochuan Tian Yuhua Zhu 36 1 0 02 Apr 2025
Physics-Informed Solution of The Stationary Fokker-Plank Equation for a Class of Nonlinear Dynamical Systems: An Evaluation Study H. Alhussein Mohammed Khasawneh M. Daqaq 26 1 0 25 Sep 2023
Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method Wensheng Li Chao Zhang Chuncheng Wang Hanting Guan Dacheng Tao DiffM PINN 18 12 0 18 May 2022
An end-to-end deep learning approach for extracting stochastic dynamical systems with $α$ -stable Lévy noise Cheng Fang Yubin Lu Ting Gao Jinqiao Duan 55 16 0 31 Jan 2022
Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks Xu Liu Wei Peng Zhiqiang Gong Weien Zhou W. Yao 27 54 0 18 Jan 2022
Solving time dependent Fokker-Planck equations via temporal normalizing flow Xiaodong Feng Li Zeng Tao Zhou AI4CE 36 25 0 28 Dec 2021
Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning Bo Lin Qianxiao Li W. Ren 29 8 0 22 Oct 2021
Extracting Stochastic Governing Laws by Nonlocal Kramers-Moyal Formulas Yubin Lu Yang Li Jinqiao Duan 21 16 0 28 Aug 2021
Learning Thermodynamically Stable and Galilean Invariant Partial Differential Equations for Non-equilibrium Flows Juntao Huang Zhiting Ma Y. Zhou W. Yong AI4CE 43 16 0 28 Sep 2020