MeshDQN：一个用于改进计算流体力学网格的深度强化学习框架
MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics
摘要：
网格划分是计算流体动力学（CFD）中稳定和准确模拟所必需的关键但用户密集的过程。网格生成是计算流体力学（CFD）管道中的一个瓶颈。自适应网格技术允许网格自动更新，以产生手头问题的准确解决方案。现有的自适应网格化的经典技术需要求解器的附加功能，许多训练模拟，或两者兼而有之。当前的机器学习技术通常需要用于训练数据生成的大量计算成本，并且在范围上受限于训练数据流态。MeshDQN是一个通用的深度强化学习框架，用于迭代粗化网格，同时保留目标属性计算。基于图神经网络的深度Q网络用于选择要移除的网格顶点，并且在改进过程中的每一步使用解插值来绕过昂贵的模拟。MeshDQN需要在网格粗化之前进行一次模拟，同时不对流态、网格类型或求解器进行假设，只需要能够直接在CFD管道中修改网格。MeshDQN成功改进了两个二维翼型的网格。
英文摘要：
Meshing is a critical, but user-intensive process necessary for stable and accurate simulations in computational fluid dynamics (CFD). Mesh generation is often a bottleneck in CFD pipelines. Adaptive meshing techniques allow the mesh to be updated automatically to produce an accurate solution for the problem at hand. Existing classical techniques for adaptive meshing require either additional functionality out of solvers, many training simulations, or both. Current machine learning techniques often require substantial computational cost for training data generation, and are restricted in scope to the training data flow regime. MeshDQN is developed as a general purpose deep reinforcement learning framework to iteratively coarsen meshes while preserving target property calculation. A graph neural network based deep Q network is used to select mesh vertices for removal and solution interpolation is used to bypass expensive simulations at each step in the improvement process. MeshDQN requires a single simulation prior to mesh coarsening, while making no assumptions about flow regime, mesh type, or solver, only requiring the ability to modify meshes directly in a CFD pipeline. MeshDQN successfully improves meshes for two 2D airfoils.
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。
