  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.
