The use of machine learning algorithms to predict behaviors of complex systems is booming. However, the key to an effective use of machine learning tools in multi-physics problems, including combustion, is to couple them to physical and computer models. The performance of these tools is enhanced if all the prior knowledge and the physical constraints are embodied. In other words, the scientific method must be adapted to bring machine learning into the picture, and make the best use of the massive amount of data we have produced, thanks to the advances in numerical computing. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems. Examples of feature extraction in turbulent combustion data, empirical low-dimensional manifold (ELDM) identification, classification, regression, and reduced-order modeling are provided.
翻译:利用机器学习算法来预测复杂系统的行为正在蓬勃发展,然而,在包括燃烧在内的多物理问题中有效利用机器学习工具的关键在于将其与物理和计算机模型结合起来。如果所有先前的知识和物理限制都得到体现,这些工具的性能就会提高。换句话说,科学方法必须加以调整,以便把机器学习带入图中,并最佳地利用我们制作的大量数据,这要归功于数字计算的进步。本章回顾了应用数据驱动的燃烧系统减序模型的一些开放机会。提供了动荡燃烧数据中的特征提取、经验性低维数识别、分类、回归和减序模型等实例。