Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading

DOI10.1007/S00466-023-02343-6zbMath1516.74028arXiv2303.18055MaRDI QIDQ6164292

Minglei Lu, Zhaoxu Meng, Gang Li, Zhen Li, Xuhui Meng, Ali Mohammadi

Publication date: 27 July 2023

Published in: Computational Mechanics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2303.18055

zbMATH Keywords

neural networkfinite element analysismachine learningadditive manufacturingdeep operator network

Mathematics Subject Classification ID

Finite element methods applied to problems in solid mechanics (74S05) Composite and mixture properties (74E30)

Cites Work

• The mechanical behaviour of interpenetrating phase composites. I: Modelling. II: A case study of a three-dimensional printed material
• B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
• Learning functional priors and posteriors from data and physics
• Smart sampling and incremental function learning for very large high dimensional data
• Finite element coupled positive definite deep neural networks mechanics system for constitutive modeling of composites
• Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems
• DeepXDE: A Deep Learning Library for Solving Differential Equations

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