Physics-Aware Machine Learning for Geosciences and Remote Sensing
Gustau Camps‐Valls, Daniel Heestermans Svendsen, Jordi Cortés-Andrés, Alvaro Mareno-Martinez, Adrián Pérez-Suay, José E. Adsuara, Irene Martín, María Piles, Jordi Muñoz-Marı́, Luca Martino
Abstract
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: encoding differential equations from data, constraining data-driven models with physics-priors and dependence constraints, improving parameterizations, emulating physical models, and blending 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.