2020/10/18 by Gustau Camps-Valls, Gustau Camps‐Valls, Daniel H. Svendsen +23 · 1 voice
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Applications (stat.AP) #Atmospheric and Oceanic Physics (physics.ao-ph) #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Scientific Computing and Data Management #Time Series Analysis and Forecasting #cs.LG #physics.ao-ph #stat.AP
paper · pdf · doi:10.48550/arxiv.2010.09031
openalex publication_date 2020/10/18 · arxiv published 2020/10/18 · arxiv updated 2020/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables relations, deriving models that are physically interpretable, that are simple parsimonious, and mathematically tractable. Machine learning models alone are excellent approximators, but very often do not respect the most elementary laws of physics, like mass or energy conservation, so consistency and confidence are compromised. In this paper, we describe the main challenges ahead in the field, and introduce several ways to live in the Physics and machine learning interplay: to encode differential equations from data, constrain data-driven models with physics-priors and dependence constraints, improve parameterizations, emulate physical models, and blend data-driven and process-based models. This is a collective long-term AI agenda towards developing and applying algorithms capable of discovering knowledge in the Earth system.