Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
Abstrak
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.
Topik & Kata Kunci
Penulis (5)
J. Willard
X. Jia
Shaoming Xu
M. Steinbach
Vipin Kumar
Akses Cepat
- Tahun Terbit
- 2020
- Bahasa
- en
- Total Sitasi
- 606×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1145/3514228
- Akses
- Open Access ✓