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physics.flu-dyn2025
Reduced-order modeling of large-scale turbulence using Koopman -variational autoencoders
Rakesh Halder, Benet Eiximeno, Oriol Lehmkuhl
Reduced-order models (ROMs) are very popular for surrogate modeling of full-order computational fluid dynamics (CFD) simulations, allowing for real-time approximation of complex fl…
physics.flu-dyn2024★ 18 cited
Reduced-order modeling of unsteady fluid flow using neural network ensembles
Rakesh Halder, Mohammadmehdi Ataei, Hesam Salehipour +2
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 (…