基于NEMoSys的智能自适应网格优化
Smart Adaptive Mesh Refinement with NEMoSys
摘要：
自适应网格加密（AMR）提供了一个实用的解决方案，以减少使用计算网格的数值模拟的计算成本和内存需求。在这项工作中，我们介绍了一种新的智能方法，自适应网格细化。智能自适应细化将经典AMR与机器学习相结合，以解决传统方法的一些已知问题。我们提供了一个自适应细化算法。随后，我们介绍了一个模块化的面向对象的结构，我们的智能AMR算法。然后，我们提出了用于训练智能AMR模型的程序。本研究随后演示了初步的数值研究，表明从计算流体动力学领域选择的几个演示问题进行自适应网格细化的可行性。最后，我们对未来的工作提出了一些意见。
英文摘要：
Adaptive mesh refinement (AMR) offers a practical solution to reduce the computational cost and memory requirement of numerical simulations that use computational meshes. In this work, we introduce a novel smart methodology for adaptive mesh refinement. Smart adaptive refinement blends classical AMR with machine learning to address some of the known issues of the conventional approaches. We provide an algorithm for adaptive refinement. Subsequently, we introduce a modular object-oriented structure for our smart AMR algorithm. Then we present procedures used for the training of a smart AMR model. The study follows with a demonstration of preliminary numerical studies indicating the feasibility of performing adaptive mesh refinement on a few demonstrative problems selected from the CFD domain. Finally, we conclude with a few comments about future work.
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