Coupled Integral PINN for Discontinuity

Fuente: arXiv
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Main Authors: Wang, Yeping, Yang, Shihao
Format: Preprint
Published: 2024
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author Wang, Yeping
Yang, Shihao
author_facet Wang, Yeping
Yang, Shihao
contents Physics-Informed Neural Networks (PINNs) solve forward PDEs by minimizing residual losses from the governing equations with initial and boundary conditions, but they often struggle with discontinuities such as shocks. In contrast, finite volume methods (FVM) handle discontinuities by enforcing integral conservation, which admits weak solutions. Motivated by this, we propose a Coupled Integral PINN (CI-PINN) that augments a standard PINN with an auxiliary network for integral potentials and coupled integral constraints. This improves robustness near shocks while avoiding meshing and the numerical flux integration/reconstruction used in classical schemes. We validate CI-PINN on forward benchmarks including Burgers, Buckley--Leverett, the Euler system, and the Shallow-Water equations.
format Preprint
id arxiv_https___arxiv_org_abs_2411_11276
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Coupled Integral PINN for Discontinuity
Wang, Yeping
Yang, Shihao
Fluid Dynamics
Machine Learning
Physics-Informed Neural Networks (PINNs) solve forward PDEs by minimizing residual losses from the governing equations with initial and boundary conditions, but they often struggle with discontinuities such as shocks. In contrast, finite volume methods (FVM) handle discontinuities by enforcing integral conservation, which admits weak solutions. Motivated by this, we propose a Coupled Integral PINN (CI-PINN) that augments a standard PINN with an auxiliary network for integral potentials and coupled integral constraints. This improves robustness near shocks while avoiding meshing and the numerical flux integration/reconstruction used in classical schemes. We validate CI-PINN on forward benchmarks including Burgers, Buckley--Leverett, the Euler system, and the Shallow-Water equations.
title Coupled Integral PINN for Discontinuity
topic Fluid Dynamics
Machine Learning
url https://arxiv.org/abs/2411.11276