Living in the Physics and Machine Learning Interplay for Earth Observation
This work tackles the problem of ensuring physical consistency in Earth observation models for researchers and practitioners, but it is incremental as it builds on existing interdisciplinary approaches.
The paper addresses the challenge of making machine learning models in Earth sciences respect physical laws like mass and energy conservation, which is crucial for accurate inferences beyond mere predictions. It introduces an agenda for integrating physics and machine learning through methods such as encoding differential equations, constraining models with physics-priors, and blending data-driven and process-based models.
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.