Physics-informed neural networks with adaptive localized artificial viscosity
From MaRDI portal
Publication:6107107
DOI10.1016/j.jcp.2023.112265arXiv2203.08802OpenAlexW4379801639MaRDI QIDQ6107107
Ming Zhong, Ulisses M. Braga-Neto, Eduardo Gildin, Emilio Jose Rocha Coutinho, Marcelo Dall'Aqua, Levi D. McClenny
Publication date: 3 July 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2203.08802
Turbulence (76Fxx) Basic methods in fluid mechanics (76Mxx) Compressible fluids and gas dynamics (76Nxx)
Cites Work
- Unnamed Item
- Multilayer feedforward networks are universal approximators
- Self-adaptive physics-informed neural networks
- A residual-based artificial viscosity finite difference method for scalar conservation laws
- Thermodynamically consistent physics-informed neural networks for hyperbolic systems
- When and why PINNs fail to train: a neural tangent kernel perspective
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- An Eulerian differencing method for unsteady compressible flow problems
- A numerical fluid dynamics calculation method for all flow speeds
- An Adaptive Multiresolution Discontinuous Galerkin Method for Time-Dependent Transport Equations in Multidimensions
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
- Residual‐based artificial viscosity for simulation of turbulent compressible flow using adaptive finite element methods
This page was built for publication: Physics-informed neural networks with adaptive localized artificial viscosity