Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
type: Item
instance of: scholarly article
title: Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
zbMATH Open document ID: 1454.65008
author: Dongkun Zhang
author: Lu Lu
author: Ling Guo
author: George Em Karniadakis
published in: Journal of Computational Physics
publication date: 2021-01-27T00:00:00Z
full work available at URL: https://arxiv.org/abs/1809.08327
Mathematics Subject Classification ID: 65C30
Mathematics Subject Classification ID: 35R30
Mathematics Subject Classification ID: 35K57
Mathematics Subject Classification ID: 60H35
Mathematics Subject Classification ID: 68T07
zbMATH DE Number: 7302167
zbMATH Keywords: physics-informed neural networks
zbMATH Keywords: uncertainty quantification
zbMATH Keywords: stochastic differential equations
zbMATH Keywords: arbitrary polynomial chaos
zbMATH Keywords: dropout
describes a project that uses: Adam
describes a project that uses: TensorFlow
describes a project that uses: SegNet
MaRDI profile type: Publication
OpenAlex ID: W2890968382
arXiv ID: 1809.08327
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DOI: 10.1016/J.JCP.2019.07.048
description: scientific article