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Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics

Loc Vu-Quoc1,*, Alexander Humer2

1 Aerospace Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA
2 Institute of Technical Mechanics, Johannes Kepler University, Linz, A-4040, Austria

* Corresponding Author: Loc Vu-Quoc. Email: email

Computer Modeling in Engineering & Sciences 2023, 137(2), 1069-1343. https://doi.org/10.32604/cmes.2023.028130

Abstract

Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional integration methods. Here, methods (1) and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3) relying on convolutional neural networks.. Pure ML methods to solve (nonlinear) PDEs are represented by Physics-Informed Neural network (PINN) methods, which could be combined with attention mechanism to address discontinuous solutions. Both LSTM and attention architectures, together with modern and generalized classic optimizers to include stochasticity for DL networks, are extensively reviewed. Kernel machines, including Gaussian processes, are provided to sufficient depth for more advanced works such as shallow networks with infinite width. Not only addressing experts, readers are assumed familiar with computational mechanics, but not with DL, whose concepts and applications are built up from the basics, aiming at bringing first-time learners quickly to the forefront of research. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics, even in well-known references. Positioning and pointing control of a large-deformable beam is given as an example.

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APA Style
Vu-Quoc, L., Humer, A. (2023). Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics. Computer Modeling in Engineering & Sciences, 137(2), 1069-1343. https://doi.org/10.32604/cmes.2023.028130
Vancouver Style
Vu-Quoc L, Humer A. Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics. Comput Model Eng Sci. 2023;137(2):1069-1343 https://doi.org/10.32604/cmes.2023.028130
IEEE Style
L. Vu-Quoc and A. Humer, "Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics," Comput. Model. Eng. Sci., vol. 137, no. 2, pp. 1069-1343. 2023. https://doi.org/10.32604/cmes.2023.028130



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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