Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow

Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow

Year:    2022

Author:    Xiaodong Feng, Li Zeng, Tao Zhou

Communications in Computational Physics, Vol. 32 (2022), Iss. 2 : pp. 401–423

Abstract

In this work, we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck (TFP) equations. It is well known that solutions of such equations are probability density functions, and thus our approach relies on modelling the target solutions with the temporal normalizing flows. The temporal normalizing flow is then trained based on the TFP loss function, without requiring any labeled data. Being a machine learning scheme, the proposed approach is mesh-free and can be easily applied to high dimensional problems. We present a variety of test problems to show the effectiveness of the learning approach.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.OA-2022-0090

Communications in Computational Physics, Vol. 32 (2022), Iss. 2 : pp. 401–423

Published online:    2022-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    23

Keywords:    Temporal normalizing flow Fokker-Planck equations adaptive density approximation.

Author Details

Xiaodong Feng

Li Zeng

Tao Zhou

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