Physics-informed machine learning: case studies for weather and climate modelling

By Karthik Kashinath, Mustafa Mustafa, Adrian Albert, J. Wu, C. Jiang, S. Esmaeilzadeh, Kamyar Azizzadenesheli, R. Wang, A. Chattopadhyay, A. Singh and 11 more. Philosophical Transactions of the Royal Society A, 2021

660 citations recorded by the source corpus.

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’.

Subfields: Atmospheric Science, Global and Planetary Change, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/86c03f6a3ae8d04d6451197a230b34d2551218a7

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Citations in the corpus

Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next (2022, background): “Transformer architecture instead could handle long-range dependencies by modeling global relationships in complex physical problems (Kashinath et al, 2021).”
Emerging opportunities and challenges for the future of reservoir computing (2024, background): “Six domains are 6G 136 – 140 , Next Generation (NG) Optical Networks 92,93,141 – 143 , Internet of Things (IoT) 61,144,145 , Green Data Center 120,146,147 , Intelligent Robots 148 – 150 and AI for Science 151 – 157 and Digital Twins 99,103,158 – 161 .”
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators (2022, background): “An emerging ﬁeld in AI applications in the sciences is Physics-informed Machine Learning [Kashinath et al., 2021].”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “…survey from other works that focus on more general knowledge integration into machine learning [7, 277] and works covering physics integration into ML in specific domains (e.g., cyber-physical systems [223], chemistry [205], medical imaging [175], fluid mechanics [46], climate and weather[146]).”
Physics-Informed Neural Operator for Learning Partial Differential Equations (2021, neutral): “[22–25].”
ClimaX: A foundation model for weather and climate (2023, neutral): “…of atmospheric variables, especially for meteorological applications (Grover et al., 2015; Dueben & Bauer, 2018; Weber et al., 2020; Scher & Messori, 2019; Scher, 2018; Kashinath et al., 2021; Schultz et al., 2021; Reichstein et al., 2019; Huntingford et al., 2019; Schneider et al., 2017).”
FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators (2022, background): “The next frontier of data-driven DL models is physics-informed DL and there has already been rapid progress in this sub-field of scientific machine learning, including in applications to weather and climate modeling [15].”