What machine learning can do for computational solid mechanics

TL;DR

This paper explores the potential of machine learning to enhance computational solid mechanics by addressing the limitations of classical methods and outlining future research directions.

Contribution

It provides a non-exhaustive overview of how machine learning can be applied to numerical modeling in solid mechanics and offers perspectives on future developments.

Findings

Machine learning can improve computational efficiency in solid mechanics.

Potential applications include modeling solids and structures more effectively.

The paper highlights future research avenues in the field.

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.