Feb. 9, 2024, 5:43 a.m. | Rakesh Halder Mohammadmehdi Ataei Hesam Salehipour Krzysztof Fidkowski Kevin Maki

cs.LG updates on arXiv.org arxiv.org

The use of deep learning has become increasingly popular in reduced-order models (ROMs) to obtain low-dimensional representations of full-order models. Convolutional autoencoders (CAEs) are often used to this end as they are adept at handling data that are spatially distributed, including solutions to partial differential equations. When applied to unsteady physics problems, ROMs also require a model for time-series prediction of the low-dimensional latent variables. Long short-term memory (LSTM) networks, a type of recurrent neural network useful for modeling sequential …

adept autoencoders become cs.lg data deep learning differential distributed flow low modeling network neural network physics.flu-dyn popular solutions

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