The quality of mesh generation has long been considered a vital aspect in providing engineers with reliable simulation results throughout the history of the Finite Element Method (FEM). The element extraction method, which is currently the most robust method, is used in business software. However, in order to speed up extraction, the approach is done by finding the next element that optimizes a target function, which can result in local mesh of bad quality after many time steps. We provide TreeMesh, a method that uses this method in conjunction with reinforcement learning (also possible with supervised learning) and a novel Monte-Carlo tree search (MCTS) (Coulom(2006), Kocsis and Szepesv\'ari(2006), Browne et~al.(2012)). The algorithm is based on a previously proposed approach (Pan et~al.(2021)). After making many improvements on DRL (algorithm, state-action-reward setting) and adding a MCTS, it outperforms the former work on the same boundary. Furthermore, using tree search, our program reveals much preponderance on seed-density-changing boundaries, which is common on thin-film materials.
翻译:长期以来,人们一直认为网目生成的质量是向工程师提供精密元素法(FEM)整个历史中可靠模拟结果的一个重要方面。元素提取方法目前是最有力的方法,在商业软件中使用。然而,为了加快提取,该方法是通过找到下一个要素来完成,该要素优化了目标功能,在经过许多步骤之后可能导致当地质量差的网格。我们提供了TreeMesh这一方法,该方法结合强化学习(也有可能与监督学习相结合)和新的Monte-Carlo树搜索(Coulom(2006年)、Kocsis和Szeperev\'ari(2006年)、Browne et 和 ~al(2012年))使用。该方法基于先前提议的方法(Pan et ~al. (2021年) ) 。在对DRL(algorithm, State-Action-Reward setting)进行多次改进后,在添加MCTS后,它超越了以前关于同一边界的工作。此外,我们的方案在树搜索中揭示了种子密度基质-file-frimilling-file-file-frimillefile,这是常见的常见。