What machine learning can do for computational solid mechanics

Abstract

Machine learning has found its way into almost every area of science and engineering, and we are only at the beginning of its exploration across fields. Being a popular, versatile and powerful framework, machine learning has proven most useful where classical techniques are computationally inefficient, which applies particularly to computational solid mechanics. Here, we dare to give a non-exhaustive overview of potential avenues for machine learning in the numerical modeling of solids and structures and offer our (subjective) perspective on what is yet to come.

11 pages, 4 figures

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• Fourier Neural Operator for Parametric Partial Differential Equations
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• Unsupervised discovery of interpretable hyperelastic constitutive laws
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• Data-driven fracture mechanics
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Cited by in corpus (7)

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• Finite electro-elasticity with physics-augmented neural networks
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics
• Deep Neural Operator Enabled Concurrent Multitask Design for Multifunctional Metamaterials under Heterogeneous Fields
• ML-based identification of the interface regions for coupling local and nonlocal models