Artificial neural network solver for time-dependent Fokker-Planck equations

DOI10.1016/j.amc.2023.128185arXiv2211.05294MaRDI QIDQ6096280

Caleb Meredith, Yao Li

Publication date: 11 September 2023

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2211.05294

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Partial differential equations of mathematical physics and other areas of application (35Qxx) Probabilistic methods, stochastic differential equations (65Cxx)

Cites Work

• On the limited memory BFGS method for large scale optimization
• Self-adaptive physics-informed neural networks
• An efficient data-driven solver for Fokker-Planck equations: algorithm and analysis
• SelectNet: self-paced learning for high-dimensional partial differential equations
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• A data-driven method for the steady state of randomly perturbed dynamics
• Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks

This page was built for publication: Artificial neural network solver for time-dependent Fokker-Planck equations