0000000000158290

AUTHOR

Irene Martin

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Physics-Aware Machine Learning For Geosciences And Remote Sensing

2021

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 knowled…

Physics010504 meteorology & atmospheric sciencesMathematical modelbusiness.industry0211 other engineering and technologies02 engineering and technologyMachine learningcomputer.software_genre01 natural sciencesField (computer science)Data modelingEnergy conservationEarth system scienceConsistency (database systems)Encoding (memory)Artificial intelligencebusinesscomputerGeology021101 geological & geomatics engineering0105 earth and related environmental sciencesPhysical lawIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium 2021
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