SRL-Assisted AFM：采用监督与强化学习辅助的推进波前法生成平面非结构化四边形网格
SRL-Assisted AFM: Generating Planar Unstructured Quadrilateral Meshes with Supervised and Reinforcement Learning-Assisted Advancing Front Method
AI总结：
针对复杂区域网格生成效率低、依赖人工的瓶颈，提出结合监督与强化学习辅助推进波前法的SRL-assisted AFM框架，可自动生成高质量平面非结构化四边形网格，监督学习预测商用软件准确率超98%。
摘要：
高质量网格生成是精确有限元分析的基础。由于内部顶点搜索空间庞大且初始边界复杂，复杂区域的网格生成需要大量人工处理，长期以来被视为整个建模与分析流程中最具挑战性且最耗时的瓶颈。本文提出一种名为“SRL-assisted AFM”的新型计算框架，用于平面几何网格划分，该框架将推进波前法与神经网络相结合，通过“策略网络”选择参考顶点并更新波前边界。这些深度神经网络采用结合监督学习与强化学习的独特流程进行训练，以迭代提升网格质量。首先，我们通过在正方形区域内随机采样点并依次连接，生成不同的初始边界，这些边界用于获取输入网格并在监督学习模块中提取训练数据集。随后，我们借助针对特殊需求设计的奖励函数迭代提升强化学习模型性能，这些需求包括提升网格质量、控制奇异点的数量与分布。我们提出的监督学习神经网络在预测商用软件结果方面准确率超过98%。最终的强化学习神经网络能够为带有尖锐特征和边界层的复杂平面区域自动生成高质量四边形网格。
英文摘要：
High-quality mesh generation is the foundation of accurate finite element analysis. Due to the vast interior vertices search space and complex initial boundaries, mesh generation for complicated domains requires substantial manual processing and has long been considered the most challenging and time-consuming bottleneck of the entire modeling and analysis process. In this paper, we present a novel computational framework named ``SRL-assisted AFM" for meshing planar geometries by combining the advancing front method with neural networks that select reference vertices and update the front boundary using ``policy networks." These deep neural networks are trained using a unique pipeline that combines supervised learning with reinforcement learning to iteratively improve mesh quality. First, we generate different initial boundaries by randomly sampling points in a square domain and connecting them sequentially. These boundaries are used for obtaining input meshes and extracting training datasets in the supervised learning module. We then iteratively improve the reinforcement learning model performance with reward functions designed for special requirements, such as improving the mesh quality and controlling the number and distribution of extraordinary points. Our proposed supervised learning neural networks achieve an accuracy higher than 98% on predicting commercial software. The final reinforcement learning neural networks automatically generate high-quality quadrilateral meshes for complex planar domains with sharp features and boundary layers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
