Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
复制标题
集成科学知识与机器学习工程和环境系统
DOI：
10.1145/3514228
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发表时间：
2020-03
影响因子：
16.6
通讯作者：
J. Willard;X. Jia;Shaoming Xu;M. Steinbach;Vipin Kumar
中科院分区：
文献类型：
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作者：
J. Willard;X. Jia;Shaoming Xu;M. Steinbach;Vipin Kumar
越来越多的人认为，复杂科学和工程问题的解决方案需要新颖的方法，能够将传统的基于物理的建模方法与最先进的机器学习（ML）技术相结合。本文提供了这类技术的结构化概述。总结了这些方法已经应用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指导的ML模型和混合物理-ML框架的方法类。然后，我们提供了这些现有技术的分类，它揭示了学科之间的知识差距和潜在的交叉方法，可以作为未来研究的想法。
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 article 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.