Parametrized polyconvex hyperelasticity with physics-augmented neural networks

In the present work, neural networks are applied to formulate parametrized hyperelastic constitutive models. The models fulfill all common mechanical conditions of hyperelasticity by construction. In particular, partially input convex neural network (pICNN) architectures are applied based on feed-fo...

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Bibliografiske detaljer

Principais autores:	Dominik K. Klein, Fabian J. Roth, Iman Valizadeh, Oliver Weeger
Format:	Artigo
Sprog:	Inglês
Udgivet:	Cambridge University Press 2023-01-01
Serier:	Data-Centric Engineering
Fag:	constitutive modeling hyperelasticity parametrized material partially input convex neural networks physics-augmented neural networks
Online adgang:	https://www.cambridge.org/core/product/identifier/S2632673623000217/type/journal_article
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