不完全到完全的多物理场预测:学习未知现象的混合方法

Nilam N. Tathawadekar, Nguyen Anh Khoa Doan, Camilo F. Silva, Nils Thuerey
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引用次数: 2

摘要

在对复杂动力系统的物理机制只有部分了解的情况下,对其进行建模是所有科学和工程学科都面临的一个关键问题。纯数据驱动的方法,仅利用人工神经网络和数据,往往不能准确地模拟系统动力学在足够长的时间内以物理一致的方式演变。因此,我们提出了一种混合方法,该方法使用神经网络模型与提供已知但不完整物理信息的不完全偏微分方程(PDEs)求解器相结合。在这项研究中,我们证明了通过所提出的混合神经网络- pde求解器模型可以在每个时间步有效地校正从不完整pde获得的结果,从而正确地考虑了系统中存在的未知物理的影响。为了验证目的,将获得的混合模型的模拟结果与描述所考虑系统的全部物理特性的完整pde集的结果成功地进行了比较。我们证明了所提出的方法在反应流上的有效性,反应流是一个典型的多物理系统,结合了流体力学和化学,后者被认为是未知的物理。对平面火焰和本生型火焰在不同工况下进行了实验。混合神经网络- pde方法在很长的时间窗口内正确地模拟了所研究情况的火焰演变,提高了泛化效果,并允许更大的模拟时间步长。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incomplete to complete multiphysics forecasting: a hybrid approach for learning unknown phenomena
Abstract Modeling complex dynamical systems with only partial knowledge of their physical mechanisms is a crucial problem across all scientific and engineering disciplines. Purely data-driven approaches, which only make use of an artificial neural network and data, often fail to accurately simulate the evolution of the system dynamics over a sufficiently long time and in a physically consistent manner. Therefore, we propose a hybrid approach that uses a neural network model in combination with an incomplete partial differential equations (PDEs) solver that provides known, but incomplete physical information. In this study, we demonstrate that the results obtained from the incomplete PDEs can be efficiently corrected at every time step by the proposed hybrid neural network—PDE solver model, so that the effect of the unknown physics present in the system is correctly accounted for. For validation purposes, the obtained simulations of the hybrid model are successfully compared against results coming from the complete set of PDEs describing the full physics of the considered system. We demonstrate the validity of the proposed approach on a reactive flow, an archetypal multi-physics system that combines fluid mechanics and chemistry, the latter being the physics considered unknown. Experiments are made on planar and Bunsen-type flames at various operating conditions. The hybrid neural network—PDE approach correctly models the flame evolution of the cases under study for significantly long time windows, yields improved generalization and allows for larger simulation time steps.
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