论文标题
将科学知识与工程和环境系统的机器学习相结合
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
论文作者
论文摘要
越来越多的共识是，解决复杂的科学和工程问题的解决方案需要能够将基于物理学的传统建模方法与最先进的机器学习（ML）技术相结合的新方法。本文提供了此类技术的结构化概述。总结了已应用这些方法的以应用程序为中心的目标领域，然后描述了用于构建物理学引导的ML模型和混合物理ML框架的方法类别。然后，我们提供了这些现有技术的分类学，该分类学发现了知识差距和学科之间的方法的潜在交叉，这些方法可以用作未来研究的思想。
There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques. This paper provides a structured overview of such techniques. Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks are described. We then provide a taxonomy of these existing techniques, which uncovers knowledge gaps and potential crossovers of methods between disciplines that can serve as ideas for future research.
