Reinforcement learning for automatic quadrilateral mesh generation: A soft actor-critic approach

This paper proposes, implements, and evaluates a reinforcement learning (RL)-based computational framework for automatic mesh generation. Mesh generation plays a fundamental role in numerical simulations in the area of computer aided design and engineering (CAD/E). It is identified as one of the critical issues in the NASA CFD Vision 2030 Study. Existing mesh generation methods suffer from high computational complexity, low mesh quality in complex geometries, and speed limitations. These methods and tools, including commercial software packages, are typically semiautomatic and they need inputs or help from human experts. By formulating the mesh generation as a Markov decision process (MDP) problem, we are able to use a state-of-the-art reinforcement learning (RL) algorithm called soft actor-critic to automatically learn from trials the policy of actions for mesh generation. The implementation of this RL algorithm for mesh generation allows us to build a fully automatic mesh generation system without human intervention and any extra clean-up operations, which fills the gap in the existing mesh generation tools. In the experiments to compare with two representative commercial software packages, our system demonstrates promising performance with respect to scalability, generalizability, and effectiveness. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents, implements, and evaluates a computational framework for automatic mesh generation based on reinforcement learning (RL). Mesh generation is crucial for numerical simulations in computer-aided design and engineering (CAD/E) and is considered a critical issue in the NASA CFD Vision 2030 Study. Existing mesh generation methods face challenges such as high computational complexity, low mesh quality in complex geometries, and limited speed. By formulating mesh generation as a Markov decision process (MDP), the paper applies a state-of-the-art RL algorithm called soft actor-critic to automatically learn the actions for mesh generation. The implementation of this RL algorithm enables the development of a fully automated mesh generation system without human intervention or additional clean-up operations, addressing the gaps in existing mesh generation tools.