Learning hyperelastic anisotropy from data via a tensor basis neural network Article Swipe

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Jan N. Fuhg , Nikolaos Bouklas , Reese E. Jones ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1016/j.jmps.2022.105022

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Anisotropy

Mathematical Analysis

Artificial Intelligence

Finite Element Method

Structural Engineering

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Hyperelastic material Anisotropy Artificial neural network Isotropy Tensor (intrinsic definition) Homogeneous space Regularization (linguistics) Boundary value problem Basis (linear algebra) Representation (politics) Mathematical analysis Mathematics Computer science Applied mathematics Geometry Physics Artificial intelligence Finite element method Structural engineering Optics Engineering Law Politics Political science

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.jmps.2022.105022
OA Status: green
Cited By: 6
References: 82
Related Works: 10
OpenAlex ID: https://openalex.org/W4223560121

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DOI: https://doi.org/10.1016/j.jmps.2022.105022

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Title: Learning hyperelastic anisotropy from data via a tensor basis neural network

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-08-11

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Authors: Jan N. Fuhg, Nikolaos Bouklas, Reese E. Jones

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Landing page: https://doi.org/10.1016/j.jmps.2022.105022

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OA URL: https://arxiv.org/pdf/2204.04529

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Concepts: Hyperelastic material, Anisotropy, Artificial neural network, Isotropy, Tensor (intrinsic definition), Homogeneous space, Regularization (linguistics), Boundary value problem, Basis (linear algebra), Representation (politics), Mathematical analysis, Mathematics, Computer science, Applied mathematics, Geometry, Physics, Artificial intelligence, Finite element method, Structural engineering, Optics, Engineering, Law, Politics, Political science

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Cited by: 6

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Citations by year (recent): 2025: 5, 2024: 1

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Related works (count): 10

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referenced_works_count	82
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