Computing high-dimensional invariant distributions from noisy data

DOI10.1016/j.jcp.2022.111783OpenAlexW4309776835MaRDI QIDQ2112468

Qianxiao Li, Weiqing Ren, Bo Lin

Publication date: 11 January 2023

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2022.111783

zbMATH Keywords

Fokker-Planck equationnoisy datamaximum likelihood methodinvariant distribution

Mathematics Subject Classification ID

Stochastic analysis (60Hxx) Partial differential equations of mathematical physics and other areas of application (35Qxx) Probability theory and stochastic processes (60-XX)

Uses Software

• Adam

Cites Work

• Computing the invariant distribution of randomly perturbed dynamical systems using deep learning
• Random Perturbations of Dynamical Systems
• Deterministic Nonperiodic Flow
• Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks
• Data-driven discovery of governing equations for fluid dynamics based on molecular simulation
• Handbook of stochastic methods for physics, chemistry and natural sciences.

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