Adaptive deep density approximation for Fokker-Planck equations
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Publication:2135831
DOI10.1016/j.jcp.2022.111080OpenAlexW3137229187MaRDI QIDQ2135831
Qifeng Liao, Xiaoliang Wan, Kejun Tang
Publication date: 9 May 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2103.11181
Stochastic analysis (60Hxx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Probabilistic methods, stochastic differential equations (65Cxx)
Related Items (9)
VAE-KRnet and Its Applications to Variational Bayes ⋮ On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements ⋮ Adaptive deep density approximation for fractional Fokker-Planck equations ⋮ Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow ⋮ DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations ⋮ Numerical computation of partial differential equations by hidden-layer concatenated extreme learning machine ⋮ AONN: An Adjoint-Oriented Neural Network Method for All-At-Once Solutions of Parametric Optimal Control Problems ⋮ Self-adaptive physics-informed neural networks ⋮ A deep domain decomposition method based on Fourier features
Uses Software
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