Reinforcement Learning and Graph Embedding for Binary Truss Topology Optimization Under Stress and Displacement Constraints

Reinforcement Learning and Graph Embedding for Binary Truss Topology Optimization Under Stress and Displacement Constraints

This paper addresses a combined method of reinforcement learning and graph embedding for binary topology optimization of trusses to minimize total structural volume under stress and displacement constraints. Although conventional deep learning methods owe their success to a convolutional neural netw...

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Bibliographic Details
Main Authors: Kazuki Hayashi, Makoto Ohsaki
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-04-01
Series:Frontiers in Built Environment
Subjects:
topology optimization
binary-type approach
machine learning
reinforcement learning
graph embedding
truss
Online Access:https://www.frontiersin.org/article/10.3389/fbuil.2020.00059/full

Internet

https://www.frontiersin.org/article/10.3389/fbuil.2020.00059/full

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