Deep Learning in Deterministic Computational Mechanics
- URL: http://arxiv.org/abs/2309.15421v1
- Date: Wed, 27 Sep 2023 05:57:19 GMT
- Title: Deep Learning in Deterministic Computational Mechanics
- Authors: Leon Herrmann, Stefan Kollmannsberger
- Abstract summary: This review focuses on deep learning methods rather than applications for computational mechanics.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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