气候变化领域集成服务门户(CCPortal): Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

CCPortal

DOI	10.1145/3514228
	Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
	Willard, Jared; Jia, Xiaowei; Xu, Shaoming; Steinbach, Michael; Kumar, Vipin
发表日期	2023
ISSN	0360-0300
EISSN	1557-7341
卷号	55 期号:4
英文摘要	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.
英文关键词	Physics-guided; neural networks; deep learning; physics-informed; theory-guided; hybrid; knowledge integration
语种	英语
WOS研究方向	Computer Science, Theory & Methods
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000890647400002
来源期刊	ACM COMPUTING SURVEYS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281342
作者单位	University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
推荐引用方式 GB/T 7714	Willard, Jared,Jia, Xiaowei,Xu, Shaoming,et al. Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems[J],2023,55(4).
APA	Willard, Jared,Jia, Xiaowei,Xu, Shaoming,Steinbach, Michael,&Kumar, Vipin.(2023).Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems.ACM COMPUTING SURVEYS,55(4).
MLA	Willard, Jared,et al."Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems".ACM COMPUTING SURVEYS 55.4(2023).