This paper provides a short overview of how to use machine learning to build data-driven models in fluid mechanics. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training data to inform the model, (3) choosing an architecture with which to represent the model, (4) designing a loss function to assess the performance of the model, and (5) selecting and implementing an optimization algorithm to train the model. At each stage, we discuss how prior physical knowledge may be embedding into the process, with specific examples from the field of fluid mechanics.
翻译:本文件简要概述了如何利用机器学习来建立流体力学中的数据驱动模型,将机器学习过程分为五个阶段:(1) 提出模型问题;(2) 收集和整理培训数据,为模型提供信息;(3) 选择一个模型代表架构;(4) 设计损失功能,以评估模型的性能;(5) 选择和实施优化算法,以培训模型;在每一个阶段,我们讨论如何将先前的物理知识纳入工艺,并举流体力学领域的具体例子。