Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
type: Item
instance of: scholarly article
title: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
zbMATH Open document ID: 1415.68175
author: Maziar Raissi
author: Paris Perdikaris
author: George Em Karniadakis
published in: Journal of Computational Physics
publication date: 2019-07-22T00:00:00Z
full work available at URL: https://www.osti.gov/biblio/1595805
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 35K57
Mathematics Subject Classification ID: 35Q55
Mathematics Subject Classification ID: 35R30
zbMATH DE Number: 7083728
zbMATH Keywords: data-driven scientific computing
zbMATH Keywords: machine learning
zbMATH Keywords: predictive modeling
zbMATH Keywords: Runge-Kutta methods
zbMATH Keywords: nonlinear dynamics
describes a project that uses: L-BFGS
describes a project that uses: TensorFlow
describes a project that uses: ImageNet
describes a project that uses: DiffSharp
describes a project that uses: Spearmint
describes a project that uses: Omniglot
describes a project that uses: AlexNet
describes a project that uses: Chebfun
describes a project that uses: Adam
MaRDI profile type: Publication
OpenAlex ID: W2899283552
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Wikidata QID: Q128976826
DOI: 10.1016/J.JCP.2018.10.045
description: scientific article