Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
This work addresses the problem of enhancing deep learning for physical modeling, which is incremental as it builds on existing methods by integrating domain-specific knowledge.
The paper tackles the challenge of modeling complex physical processes like sea surface temperature prediction by incorporating prior scientific knowledge into deep learning models, demonstrating improved performance through experiments and comparisons with baselines including a state-of-the-art numerical approach.
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.