Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks
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Publication:2060125
DOI10.1016/j.cma.2021.114217OpenAlexW3160138438MaRDI QIDQ2060125
Michele Marino, Nikolaos Bouklas, Jan N. Fuhg
Publication date: 13 December 2021
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2105.04554
Artificial neural networks and deep learning (68T07) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60)
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