- Published
- 2021-02-15
- Venue
- Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, vol. 379, no. 2194, pp. 20200093
- Cited by
- 668
- Open access
- No free copy found
- DOI
- 10.1098/rsta.2020.0093
- PubMed
- 33583262
Abstract
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'.
Cite this paper
Formatted from the source metadata, not typed by hand. For a whole reference list, use the citation generator or ask your assistant through Find Research.
- APA 7
Kashinath, K., Mustafa, M., Albert, A., Wu, J., Jiang, C.-H., Esmaeilzadeh, S., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., Singh, A., Manepalli, A., Chirila, D. B., Yu, R., Walters, R., White, B. L., Xiao, H., Tchelepi, H. A., Marcus, P., Anandkumar, A., … Prabhat. (2021). Physics-informed machine learning: case studies for weather and climate modelling. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 379(2194), 20200093. https://doi.org/10.1098/rsta.2020.0093
- MLA 9
Kashinath, Karthik, et al. "Physics-informed machine learning: case studies for weather and climate modelling." Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, vol. 379, no. 2194, 2021, pp. 20200093, https://doi.org/10.1098/rsta.2020.0093.
- Chicago
Kashinath, Karthik, M. Mustafa, Adrian Albert, Jinlong Wu, Chung-Hsiang Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, et al. "Physics-informed machine learning: case studies for weather and climate modelling." Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences 379, no. 2194 (2021): 20200093. https://doi.org/10.1098/rsta.2020.0093.
- GOST
Kashinath K., Mustafa M., Albert A. [и др.] Physics-informed machine learning: case studies for weather and climate modelling // Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. — 2021. — Vol. 379, № 2194. — P. 20200093. — URL: https://doi.org/10.1098/rsta.2020.0093
- BibTeX
@article{kashinath2021physicsinformed, title = {{Physics-informed machine learning: case studies for weather and climate modelling}}, author = {Karthik Kashinath and M. Mustafa and Adrian Albert and Jinlong Wu and Chung-Hsiang Jiang and Soheil Esmaeilzadeh and Kamyar Azizzadenesheli and Rui Wang and Ashesh Chattopadhyay and Aakanksha Singh and A. Manepalli and Dragos B. Chirila and Rose Yu and Robin Walters and Brian L. White and Heng Xiao and Hamdi A. Tchelepi and Philip Marcus and Animashree Anandkumar and Pedram Hassanzadeh and Prabhat}, journal = {Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences}, year = {2021}, volume = {379}, number = {2194}, pages = {20200093}, doi = {10.1098/rsta.2020.0093}, url = {https://doi.org/10.1098/rsta.2020.0093} }- RIS
TY - JOUR AU - Kashinath, Karthik AU - Mustafa, M. AU - Albert, Adrian AU - Wu, Jinlong AU - Jiang, Chung-Hsiang AU - Esmaeilzadeh, Soheil AU - Azizzadenesheli, Kamyar AU - Wang, Rui AU - Chattopadhyay, Ashesh AU - Singh, Aakanksha AU - Manepalli, A. AU - Chirila, Dragos B. AU - Yu, Rose AU - Walters, Robin AU - White, Brian L. AU - Xiao, Heng AU - Tchelepi, Hamdi A. AU - Marcus, Philip AU - Anandkumar, Animashree AU - Hassanzadeh, Pedram AU - Prabhat TI - Physics-informed machine learning: case studies for weather and climate modelling JO - Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences VL - 379 IS - 2194 SP - 20200093 PY - 2021 DA - 2021/02/15 DO - 10.1098/rsta.2020.0093 UR - https://doi.org/10.1098/rsta.2020.0093 ER -
Ask Claude about this paper
Add Find Research to Claude or ChatGPT and ask in plain language. The assistant reads this record, finds the papers around it and cites only what it actually found:
- “Summarize doi:10.1098/rsta.2020.0093 and list its main limitations.”
- “Find newer studies that test the same question, with free PDFs.”
- “Cite this and five related papers in APA for my literature review.”
Setup takes about a minute: add the connector, then start the free trial.
Record from OpenAlex. Find Research links only to legal copies.