Explainability, uncertainty & physics

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

K. Kashinath, M. Mustafa, A. Albert, J-L. Wu, C. Jiang, S. Esmaeilzadeh, K. Azizzadenesheli, R. Wang, A. Chattopadhyay, A. Singh, A. Manepalli, D. Chirila et al.

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · 15 February 2021 · California Institute of Technology (Caltech), Lawrence Berkeley National Laboratory / NERSC, NVIDIA, Stanford University, University of California, Berkeley

Paper

In one sentence. Surveys physics-informed ML approaches with ten case studies for emulating, downscaling and forecasting weather and climate, reporting greater consistency and data efficiency.

Labels

Topic

Explainability, uncertainty & physics, Climate emulation

Type

Review, survey or tutorial

Method

Physics-informed / physics-constrained

Domain

Global

Status

Research

Identifiers

arXiv:2020.0093 DOI:10.1098/rsta.2020.0093 OpenAlex:W3133294546

Added

2026-10-22

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