Physics informed neural networks: a case study for gas transport problems

DOI10.1016/j.jcp.2023.112041OpenAlexW4323267210MaRDI QIDQ2699348

Marc E. Pfetsch, Erik Laurin Strelow, Jens Lang, Alf Gerisch

Publication date: 26 April 2023

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2023.112041

zbMATH Keywords

conservation lawsEuler equationsgas flowmulti-criteria optimizationphysics informed neural network

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical analysis (65-XX)

Uses Software

• DeepONet
• NSFnets

Cites Work

• An implicit box scheme for subsonic compressible flow with dissipative source term
• Adaptive refinement strategies for the simulation of gas flow in networks using a model hierarchy
• Optimally weighted loss functions for solving PDEs with neural networks
• NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
• Thermodynamically consistent physics-informed neural networks for hyperbolic systems
• Model order reduction for gas and energy networks
• Physics-informed neural networks for high-speed flows
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Finite Volume Methods for Hyperbolic Problems
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
• Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs