Living in the Physics and Machine Learning Interplay for Earth Observation

Abstract

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.

arXiv comment24 pages, 10 figures, 3 tables, expanded AAAI PGAI 2020 Symposium

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Cited by in corpus (2)

• Towards a Collective Agenda on AI for Earth Science Data Analysis
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling