Physics-informed machine learning: case studies for weather and climate modelling
| Autor: | Dragos B. Chirila, Soheil Esmaeilzadeh, Adrian Albert, Kamyar Azizzadenesheli, Prabhat, Rose Yu, Karthik Kashinath, Heng Xiao, Hamdi A. Tchelepi, Ashesh Chattopadhyay, Brian White, Rui Wang, A. Singh, Animashree Anandkumar, Chiyu \\'Max\\' Jiang, Jin-Long Wu, Ashray Manepalli, Philip Marcus, Pedram Hassanzadeh, Robin Walters, Mustafa Mustafa |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
business.industry General Mathematics General Engineering Physical system General Physics and Astronomy Weather and climate Machine learning computer.software_genre Consistency (database systems) Resource (project management) Data efficiency Domain knowledge Artificial intelligence business computer Downscaling |
| Zdroj: | Philosophical transactions. Series A, Mathematical, physical, and engineering sciences. 379(2194) |
| ISSN: | 1471-2962 |
| Popis: | Machine learning (ML) provides novel and powerful ways of accurately and efficiently recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio-temporal evolution of weather and climate processes. Off-the-shelf ML models, however, do not necessarily obey the fundamental governing laws of physical systems, nor do they generalize well to scenarios on which they have not been trained. We survey systematic approaches to incorporating physics and domain knowledge into ML models and distill these approaches into broad categories. Through 10 case studies, we show how these approaches have been used successfully for emulating, downscaling, and forecasting weather and climate processes. The accomplishments of these studies include greater physical consistency, reduced training time, improved data efficiency, and better generalization. Finally, we synthesize the lessons learned and identify scientific, diagnostic, computational, and resource challenges for developing truly robust and reliable physics-informed ML models for weather and climate processes.This article is part of the theme issue ‘Machine learning for weather and climate modelling’. |
| Databáze: | OpenAIRE |
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