Adaptive deep density approximation for Fokker-Planck equations

Abstract

In this paper we present an adaptive deep density approximation strategy based on KRnet (ADDA-KR) for solving the steady-state Fokker-Planck (F-P) equations. F-P equations are usually high-dimensional and defined on an unbounded domain, which limits the application of traditional grid based numerical methods. With the Knothe-Rosenblatt rearrangement, our newly proposed flow-based generative model, called KRnet, provides a family of probability density functions to serve as effective solution candidates for the Fokker-Planck equations, which has a weaker dependence on dimensionality than traditional computational approaches and can efficiently estimate general high-dimensional density functions. To obtain effective stochastic collocation points for the approximation of the F-P equation, we develop an adaptive sampling procedure, where samples are generated iteratively using the approximate density function at each iteration. We present a general framework of ADDA-KR, validate its accuracy and demonstrate its efficiency with numerical experiments.

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• Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
• Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks
• On Computing the Hyperparameter of Extreme Learning Machines: Algorithm and Application to Computational PDEs, and Comparison with Classical and High-Order Finite Elements
• Solving time dependent Fokker-Planck equations via temporal normalizing flow
• Solving Non-local Fokker-Planck Equations by Deep Learning
• Deep adaptive sampling for surrogate modeling without labeled data
• Augmented KRnet for density estimation and approximation