Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
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 the 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.
 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.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
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 the 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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 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.
assistant
 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.