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Article summary:
1. This article presents a deep learning approach to infer physical functions based on data, specifically focusing on the temporal evolution of complex functions that arise in the context of fluid flows.
2. The method encodes multiple steps of a simulation field into a reduced latent representation with a convolutional neural network and uses an LSTM network to predict the latent space code for one or more future time steps.
3. The article evaluates training modalities and demonstrates that the setup for computing this reduced representation strongly influences how well the time network can predict changes over time.
Article analysis:
The article is written in an objective manner and provides evidence to support its claims. It is clear that the authors have conducted extensive research into existing works related to their topic, providing detailed descriptions of these works as well as their own contributions. The authors also provide evidence for their claims by demonstrating the generality of their approach with several liquid and single-phase problems, as well as by evaluating different training modalities. Furthermore, they acknowledge potential risks associated with their work, such as overfitting or generalization issues, and suggest ways to mitigate them.
In conclusion, this article is reliable and trustworthy due to its objective tone and thorough research into existing works related to its topic.