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Article summary:
1. A novel hybrid partitioned deep learning framework for reduced-order modeling of moving interfaces and predicting fluid–structure interaction.
2. Combines two separate data-driven models for fluid and solid subdomains via deep learning-based reduced-order models (DL-ROMs).
3. A popular prototypical fluid–structure interaction problem of flow past a freely oscillating cylinder is considered to assess the efficacy of the proposed methodology.
Article analysis:
The article presents a novel hybrid partitioned deep learning framework for reduced-order modeling of moving interfaces and predicting fluid–structure interaction. The article is well written, with clear explanations of the methodology used and its results. The authors provide evidence to support their claims, such as the use of proper orthogonal decomposition-based recurrent neural network (POD-RNN) as a DL-ROM procedure to infer the point cloud with a moving interface, and convolutional autoencoder network (CRAN) as a self-supervised DL-ROM procedure to infer the nonlinear flow dynamics at static Eulerian probes. The authors also provide an example of a prototypical fluid–structure interaction problem to demonstrate the efficacy of their proposed methodology.
The article does not appear to be biased or one sided in its reporting, nor does it contain any promotional content or partiality towards any particular viewpoint or opinion. All possible risks associated with this method are noted in the article, such as potential errors in force quantification along the fluid–solid interface due to low resolution DL grid search. Furthermore, both sides of the argument are presented equally throughout the article, providing an unbiased overview of this new methodology.
In conclusion, this article appears to be reliable and trustworthy in its reporting on this new hybrid partitioned deep learning framework for reduced order modeling of moving interfaces and predicting fluid–structure interaction.