On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic...
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Contents
Data-driven constitutive modeling is an emerging field in computational solid mechanics with the prospect of significantly relieving the computational costs of hierarchical computational methods. Traditionally, these surrogates have been trained using datasets which map strain inputs to stress outputs directly. Data-driven constitutive models for e...
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Full title
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
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TN_cdi_proquest_journals_2576119692
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2576119692
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E-ISSN
2331-8422
DOI
10.48550/arxiv.2109.11028
