Loc Vu-Quoc1,*, Alexander Humer2
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI: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… More >
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