论文标题
关于湍流建模中机器学习方法的观点
A Perspective on Machine Learning Methods in Turbulence Modelling
论文作者
论文摘要
这项工作对数据驱动的湍流封闭建模中的当前研究状态进行了回顾。它提供了有关挑战和开放问题的观点，还可以从应用于参数估计，模型识别，封闭术语重建及其他方面的机器学习方法的优点和承诺，主要是从大型涡流模拟和相关技术的角度来看。我们强调的是，培训数据，模型，基础物理和离散化的一致性是成功的ML夸大建模策略需要考虑的关键问题。为了使讨论对这两个领域的非专家有用，我们以简洁而自搭配的方式介绍了湍流中的建模问题以及突出的ML范式和方法。随后，我们介绍了当前数据驱动的模型概念和方法的调查，重点介绍了重要的发展，并将其置于讨论的挑战的背景下。
This work presents a review of the current state of research in data-driven turbulence closure modeling. It offers a perspective on the challenges and open issues, but also on the advantages and promises of machine learning methods applied to parameter estimation, model identification, closure term reconstruction and beyond, mostly from the perspective of Large Eddy Simulation and related techniques. We stress that consistency of the training data, the model, the underlying physics and the discretization is a key issue that needs to be considered for a successful ML-augmented modeling strategy. In order to make the discussion useful for non-experts in either field, we introduce both the modeling problem in turbulence as well as the prominent ML paradigms and methods in a concise and self-consistent manner. Following, we present a survey of the current data-driven model concepts and methods, highlight important developments and put them into the context of the discussed challenges.
