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arXiv 2401.08352math.NAcs.NA
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多孔介质多物理过程模拟的自动求解器选择

Automated solver selection for simulation of multiphysics processes in porous media

Yury Zabegaev, Eirik Keilegavlen, Einar Iversen, Inga Berre

AI总结:

针对多孔介质多物理模拟中求解器选择困难的问题,提出一种基于机器学习的框架,能根据历史数据自动搜索最优求解器、在线优化参数并随物理状态变化动态切换求解器,性能优于现有方法。

摘要:

多孔介质过程涉及机械变形、输运和流体流动等多种物理现象。精确的模拟必须捕捉这些现象之间的强耦合。为多物理问题选择高效的求解器通常需要将其解耦为与各个物理现象相关的子问题,然后为每个子问题选择合适的求解器及迭代方案。求解器组件的广泛选项使得寻找最优解变得困难且耗时;此外,求解器带有需要优化的数值参数。更复杂的是,求解器性能可能取决于模拟模型的物理状态,且该状态可能随时间变化。根据主导过程切换求解器可能是有益的,但何时切换求解器的阈值尚不明确且分析复杂。我们通过开发一个机器学习框架来应对这一挑战,该框架基于先前已解决问题的统计数据,自动为给定的多物理模拟设置搜索最优求解器。对于一系列问题(以时间依赖模拟中的连续时间步为例),该框架在模拟过程中在线更新并改进其决策模型。我们展示了它在测试问题设置中如何优于预先选定的最先进求解器。这些示例基于poromechanics模拟以及流动和输运模拟。对于准静态线性Biot模型,我们通过展示所谓的Fixed-Stress预处理器的L参数如何被优化,证明了数值求解器参数的自动调优。受一个主要传热机制在convection和diffusion之间变化的测试示例启发,我们讨论了当主导物理现象随时间变化时,求解器选择器如何动态切换求解器。

英文摘要:

Porous media processes involve various physical phenomena such as mechanical deformation, transport, and fluid flow. Accurate simulations must capture the strong couplings between these phenomena. Choosing an efficient solver for the multiphysics problem usually entails the decoupling into subproblems related to separate physical phenomena. Then, the suitable solvers for each subproblem and the iteration scheme must be chosen. The wide range of options for the solver components makes finding the optimum difficult and time-consuming; moreover, solvers come with numerical parameters that need to be optimized. As a further complication, the solver performance may depend on the physical regime of the simulation model, which may vary with time. Switching a solver with respect to the dominant process can be beneficial, but the threshold of when to switch solver is unclear and complicated to analyze. We address this challenge by developing a machine learning framework that automatically searches for the optimal solver for a given multiphysics simulation setup, based on statistical data from previously solved problems. For a series of problems, exemplified by successive time steps in a time-dependent simulation, the framework updates and improves its decision model online during the simulation. We show how it outperforms preselected state-of-the-art solvers for test problem setups. The examples are based on simulations of poromechanics and simulations of flow and transport. For the quasi-static linear Biot model, we demonstrate automated tuning of numerical solver parameters by showing how the L-parameter of the so-called Fixed-Stress preconditioner can be optimized. Motivated by a test example where the main heat transfer mechanism changes between convection and diffusion, we discuss how the solver selector can dynamically switch solvers when the dominant physical phenomenon changes with time.

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