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“Deep reinforcement learning for engineering design through topology optimization of elementally discretized design domains” by Nathan K. Brown; Anthony P. Garland; Georges M. Fadel; Gang Li is a Engineering article available to read on EtoBox.

What is “Deep reinforcement learning for engineering design through topology optimization of elementally discretized design domains” about?

Advances in machine learning algorithms and increased computational efficiencies give engineers new capabilities and tools to apply to engineering design. Machine learning models can approximate complex functions and, therefore, can be useful for various tasks in the engineering design workflow. This paper investigates using reinforcement learning (RL), a subset of machine learning that teaches an agent to complete a task through accumulating experiences in an interactive environment, to automate the designing of 2D discretized topologies. RL agents use past experiences to learn sequential sets of actions to best achieve some objective. In the proposed environment, an RL agent can make sequential decisions to design a topology by removing elements to best satisfy compliance minimization objectives. After each action, the agent receives feedback by evaluating how well the current topology satisfies the design objectives. After training, the agent was tasked with designing optimal topologies under various load cases. The agent's proposed designs had similar or better compliance minimization performance to those produced by traditional gradient-based topology optimization methods. The

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Author

Nathan K. Brown; Anthony P. Garland; Georges M. Fadel; Gang Li

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Engineering (Physical Sciences)