Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

DOI10.4208/cicp.OA-2020-0194zbMath1473.65124arXiv1905.11079OpenAlexW3100024803MaRDI QIDQ5162375

Zichao Long, Yufei Wang, Ziju Shen, Bin Dong

Publication date: 2 November 2021

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

Full work available at URL: https://arxiv.org/abs/1905.11079

zbMATH Keywords

conservation lawsfinite difference approximationWENOdeep reinforcement learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Applications of optimal control and differential games (49N90) Finite difference methods for initial value and initial-boundary value problems involving PDEs (65M06)

Related Items (7)

Convergence Rate Analysis for Deep Ritz Method ⋮ Neural control of discrete weak formulations: Galerkin, least squares \& minimal-residual methods with quasi-optimal weights ⋮ Deep reinforcement learning for adaptive mesh refinement ⋮ Error analysis of deep Ritz methods for elliptic equations ⋮ A learned conservative semi-Lagrangian finite volume scheme for transport simulations ⋮ On mathematical modeling in image reconstruction and beyond ⋮ Enhanced fifth order WENO shock-capturing schemes with deep learning

Uses Software

• DGM

Cites Work

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