PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
type: Item
instance of: scholarly article
title: PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
author: Han Gao
author: Luning Sun
author: Jian-Xun Wang
published in: Journal of Computational Physics
publication date: 2022-04-21T00:00:00Z
full work available at URL: https://arxiv.org/abs/2004.13145
Mathematics Subject Classification ID: 68-XX
Mathematics Subject Classification ID: 35-XX
zbMATH DE Number: 7511433
zbMATH Keywords: physics-informed neural networks
zbMATH Keywords: label-free
zbMATH Keywords: surrogate modeling
zbMATH Keywords: physics-constrained deep learning
zbMATH Keywords: partial differential equations
zbMATH Keywords: Navier-Stokes
Wikidata QID: Q114163480
describes a project that uses: NSFnets
describes a project that uses: PhyCNN
describes a project that uses: VarNet
describes a project that uses: PyTorch
describes a project that uses: D3M
describes a project that uses: MgNet
describes a project that uses: DGM
describes a project that uses: DeepONet
describes a project that uses: hp-VPINNs
describes a project that uses: OpenFOAM
describes a project that uses: DeepXDE
describes a project that uses: Adam
describes a project that uses: PDE-Net
MaRDI profile type: Publication
OpenAlex ID: W3111914315
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DOI: 10.1016/J.JCP.2020.110079
description: scientific article