Deep Learning in Deterministic Computational Mechanics

TL;DR

This paper provides an accessible overview of deep learning methods applied to deterministic computational mechanics, categorizing key approaches to help new researchers understand the field's landscape.

Contribution

It categorizes and explains deep learning techniques in computational mechanics, focusing on methods rather than applications, to assist newcomers in understanding the core concepts.

Findings

Identifies five main categories of deep learning in the field.

Provides simplified explanations suitable for newcomers.

Highlights potential research directions in the field.

Abstract

The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies within this field, we provide an overview of deep learning in deterministic computational mechanics. Five main categories are identified and explored: simulation substitution, simulation enhancement, discretizations as neural networks, generative approaches, and deep reinforcement learning. This review focuses on deep learning methods rather than applications for computational mechanics, thereby enabling researchers to explore this field more effectively. As such, the review is not necessarily aimed at researchers with extensive knowledge of deep learning -- instead, the primary audience is researchers at the verge of entering this field or those who attempt to gain an overview of deep learning in computational mechanics. The discussed concepts are, therefore, explained as simple as possible.