基于机器学习的网格移动用于非静力学 tsunami 模拟
Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation
AI总结：
本文提出基于机器学习的网格移动方法，用于提高非静力学 tsunami 模拟的效率和准确性。
摘要：
本研究探讨了基于机器学习的网格自适应性，特别是网格移动方法（UM2N），与深度积分非静力学浅水模型的结合。这种需求来自于对在概率海岸灾害评估中平衡效率和准确性的模型的需求。实现基于不连续 Galerkin 有限元（DG-FE）软件 Thetis，该软件利用偏微分方程（PDE）框架 Firedrake 进行自动化代码生成。通过基准测试案例验证和对海岸灾害实验室测量的验证，重点研究了 tsunami 传播、上岸和淹没。在这些测试中，UM2N 驱动的网格有助于解析关键非静力学动力学，并产生与参考计算和测量数据一致的数值解。数值结果表明，基于 UM2N 替代理的方案显著加速了传统网格移动技术，并在长时间积分和强非线性波条件下表现出高鲁棒性。
英文摘要：
This study investigates the use of machine learning based mesh movement method, specifically the Universal Mesh Movement Network (UM2N), with depth integrated non-hydrostatic shallow water models. Motivation for this comes from the need for models which balance efficiency and accuracy for use in probabilistic coastal hazard assessment. Implementations are built on the discontinuous Galerkin finite-element (DG-FE) based software, Thetis, which leverages the partial differential equation (PDE) framework Firedrake for automated code generation. Verification on benchmark test cases and validation against laboratory measurements of coastal hazards, focusing on tsunami propagation, run-up, and inundation is performed. In these tests, the UM2N-driven meshes help resolve key non-hydrostatic dynamics including wave refraction over a conical shoal, run-up with wetting-drying on a conical island, and tsunami inundation in the Monai Valley laboratory benchmark, and yield numerical solutions in close agreement with reference fine-mesh computations and measured data. Notably, in the Monai Valley case, UM2N achieves a ~91% reduction in wave-peak error at the nearshore gauge compared with ~74% for the conventional Monge--Ampère (MA) mesh movement, both relative to the coarse fixed mesh. The UM2N surrogate based approach accelerates the conventional mesh movement step, achieving a ~32% reduction in total runtime and ~2 times speed-up in mesh movement step time over the MA solver on GPU, while offering a significant improvement in robustness over long integration periods and under strongly nonlinear wave conditions.
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。
