Physics-Informed Holomorphic Neural Networks (PIHNNs): Solving 2D linear elasticity problems

We propose physics-informed holomorphic neural networks (PIHNNs) as a method to solve boundary value problems where the solution can be represented via holomorphic functions. Specifically, we consider the case of plane linear elasticity and, by leveraging the Kolosov–Muskhelishvili representation of...

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Bibliographic Details

Published in:	Computer methods in applied mechanics and engineering Vol. 432; p. 117406
Main Authors:	Calafà, Matteo, Hovad, Emil, Engsig-Karup, Allan P., Andriollo, Tito
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-12-2024
Subjects:	Complex-valued neural networks Kolosov–Muskhelishvili representation Linear elasticity Physics-informed neural networks Scientific machine learning Kolosov–Muskhelishvili representation Scientific machine learning Linear elasticity Complex-valued neural networks Physics-informed neural networks
Online Access:	Get full text
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