Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge

Abstract

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.

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• FlowNet: Learning Optical Flow with Convolutional Networks
• FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
• Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness
• Video Pixel Networks
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