Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
| Autor: | Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari |
|---|---|
| Přispěvatelé: | Machine Learning and Information Access (MLIA), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Criteo [Paris], Pajot, Arthur |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] Sociology of scientific knowledge 010504 meteorology & atmospheric sciences Computer Science - Artificial Intelligence Machine Learning (stat.ML) 010501 environmental sciences 01 natural sciences Field (computer science) Machine Learning (cs.LG) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Statistics - Machine Learning Natural (music) 0105 earth and related environmental sciences Mathematics Class (computer programming) Generality business.industry Deep learning Statistical and Nonlinear Physics Data science Variety (cybernetics) Computer Science - Learning Artificial Intelligence (cs.AI) Artificial intelligence State (computer science) Statistics Probability and Uncertainty business |
| Popis: | We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge more traditional approaches elaborated over the years in fields like maths or physics. However, despite considerable successes in a variety of application domains, the machine learning field is not yet ready to handle the level of complexity required by such problems. Using an example application, namely Sea Surface Temperature Prediction, we show how general background knowledge gained from physics could be used as a guideline for designing efficient Deep Learning models. In order to motivate the approach and to assess its generality we demonstrate a formal link between the solution of a class of differential equations underlying a large family of physical phenomena and the proposed model. Experiments and comparison with series of baselines including a state of the art numerical approach is then provided. |
| Databáze: | OpenAIRE |
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