论文标题
自动四边形网格生成的强化学习：一种软批评的方法
Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approach
论文作者
论文摘要
本文提出，实施和评估了基于自动网格生成的基于强化学习（RL）的计算框架。网格生成在计算机辅助设计和工程（CAD/E）领域的数值模拟中起着基本作用。它被确定为NASA CFD Vision 2030研究中的关键问题之一。现有的网格生成方法具有高计算复杂性，复杂几何形状的低网格质量以及速度限制。这些方法和工具，包括商业软件包，通常是半自动的，它们需要人类专家的意见或帮助。通过将网格生成作为马尔可夫决策过程（MDP）问题，我们能够使用称为“软参与者 - 批评者”的最先进的加固学习（RL）算法，从试验中自动学习，以使网格生成的行动政策。对于网格生成的这种RL算法的实现使我们能够在不干预和任何额外的清理操作的情况下构建一个全自动网格生成系统，从而填补了现有的网格生成工具的空白。在与两个代表性的商业软件包相比的实验中，我们的系统在可伸缩性，可推广性和有效性方面表现出了有希望的性能。
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.
