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Deep deterministic policy gradient and graph attention network for geometry optimization of latticed shells

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Abstract

This paper proposes a combined approach of deep deterministic policy gradient (DDPG) and graph attention network (GAT) to the geometry optimization of latticed shells with surface shapes defined by a Bézier control net. The optimization problem is formulated to minimize the strain energy of the latticed structures with heights of the Bézier control points as design variables. The information of the latticed shells, including nodal configurations, element properties and internal forces, and the Bézier control net, consisting of control points and control net, are represented as graphs using node feature matrices, adjacency matrices, and weighted adjacency matrices. A specifically designed DDPG agent utilizes GAT and matrix manipulations to observe the state of the structure through the graphs, and decides which and how Bézier control points to move. The agent is trained to excel in the task through a reward signal computed from changes in the strain energy in each optimization step. As shown in numerical examples, the trained agent can effectively optimize structures of different sizes, control nets, configurations, and initial geometries from those used during the training. The performance of the trained agent is competitive compared to particle swarm optimization and simulated annealing despite using a lower computational cost.

Highlights

- A method using a reinforcement learning agent is proposed to optimize the geometry of latticed structures.

- The agent is designed to observe the structure and Bézier control net and modify the Bézier control net.

- The method yields good results using fewer computations when compared to other conventional methods.

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Experiment data from this study are accessible to corresponding authors upon request.

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Acknowledgments

Japan ministry of education, culture, sports, science, and technology (MEXT) scholarship [grant number 180136] and Japan society for the promotion of science (JSPS) KAKENHI [grant numbers JP 20H04467, JP 21 K20461] helped fund this research.

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Authors and Affiliations

  1. Department of Architecture and Architectural Engineering, Graduate School of Engineering, Kyoto University, Kyoto, Japan

    Chi-tathon Kupwiwat, Kazuki Hayashi & Makoto Ohsaki

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  1. Chi-tathon Kupwiwat
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Correspondence to Chi-tathon Kupwiwat.

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Kupwiwat, Ct., Hayashi, K. & Ohsaki, M. Deep deterministic policy gradient and graph attention network for geometry optimization of latticed shells. Appl Intell 53, 19809–19826 (2023). https://doi.org/10.1007/s10489-023-04565-w

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