Physics-informed machine learning: case studies for weather and climate modelling.
Philos Trans A Math Phys Eng Sci
; 379(2194): 20200093, 2021 Apr 05.
Article
em En
| MEDLINE
| ID: mdl-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'.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Philos Trans A Math Phys Eng Sci
Assunto da revista:
BIOFISICA
/
ENGENHARIA BIOMEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos