A trial solution for imposing boundary conditions of partial differential equations in physics-informed neural networks-Reference-Cited by-同舟云学术

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A trial solution for imposing boundary conditions of partial differential equations in physics-informed neural networks

Published:2024-01 Issue: Volume:127 Page:107236
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Manavi Seyedalborz,Fattahi Ehsan^ORCID,Becker Thomas

Funder

Allianz Industrie Forschung

BMWK

Bundesministerium für Wirtschaft und Klimaschutz

Forschungskreis der Ernährungsindustrie

BMBF Berlin

Bundesministerium für Bildung und Forschung

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference33 articles.

1. A unified deep artificial neural network approach to partial differential equations in complex geometries;Berg;Neurocomputing,2018

2. Meta-action reliability-based mechanical product optimization design under uncertainty environment;Chen;Eng. Appl. Artif. Intell.,2021

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