Polyconvex neural network models of thermoelasticity Article Swipe
Jan N. Fuhg
,
Asghar Jadoon
,
Oliver Weeger
,
Daniel Seidl
,
Reese E. Jones
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.15562
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.15562
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive constraints, which also allows them to generalize well to unseen data. In this work, we extend a polyconvex hyperelastic neural network framework to thermo-hyperelasticity by specifying the thermodynamic and material theoretic requirements for an expansion of the Helmholtz free energy expressed in terms of deformation invariants and temperature. Different formulations which a priori ensure polyconvexity with respect to deformation and concavity with respect to temperature are proposed and discussed. The physics-augmented neural networks are furthermore calibrated with a recently proposed sparsification algorithm that not only aims to fit the training data but also penalizes the number of active parameters, which prevents overfitting in the low data regime and promotes generalization. The performance of the proposed framework is demonstrated on synthetic data, which illustrate the expected thermomechanical phenomena, and existing temperature-dependent uniaxial tension and tension-torsion experimental datasets.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.15562
- https://arxiv.org/pdf/2404.15562
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4395482630Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2404.15562Digital Object Identifier
- Title
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Polyconvex neural network models of thermoelasticityWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-23Full publication date if available
- Authors
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Jan N. Fuhg, Asghar Jadoon, Oliver Weeger, Daniel Seidl, Reese E. JonesList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.15562Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2404.15562Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2404.15562Direct OA link when available
- Concepts
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Artificial neural network, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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