text: The paper ‘Deep learning for Physical Process: incorporating prior physical knowledge’ proposes to question the use of data-intensive strategies such as deep learning in solving physical inverse problems that are traditionally solved through assimilation strategies.  They notably show how physical priors on a given phenomenon can be incorporated in the learning process and propose an application on the problem of estimating sea surface temperature directly from a given collection of satellite images. All in all the paper is very clear and interesting.  The results obtained on the considered problem are clearly of great interest, especially when compared to state-of-the-art assimilation strategies such as the one of Béréziat.  While the learning architecture is not original in itself,  it is shown that a proper physical regularization greatly improves the performance.  For these reasons I believe the paper has sufficient merits to be published at ICLR.  That being said, I believe that some discussions could strengthen the paper:  - Most classical variational assimilation schemes are stochastic in nature, notably by incorporating uncertainties in the observation or physical evolution models.  It is still unclear how those uncertainties can be integrated in the model;  - Assimilation methods are usually independent of the type of data at hand.  It is not clear how the model learnt on one particular type of data transpose to other data sequences.  Notably, the question of transfer and generalization is of high relevance here.  Does the learnt model performs well on other dataset (for instance, acquired on a different region or at a distant time).  I believe this type of issue has to be examinated for this type of approach to be widely use in inverse physical problems.