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physics.flu-dyn2025
Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system
Philipp Teutsch, Philipp Pfeffer, Mohammad Sharifi Ghazijahani +3
In recent years, data-driven deep learning models have gained significant interest in the analysis of turbulent dynamical systems. Within the context of reduced-order models (ROMs)…
physics.flu-dyn2024★ 1 cited
Comparison of Generative Learning Methods for Turbulence Surrogates
Claudia Drygala, Edmund Ross, Mohammad Sharifi Ghazijahani +3
Numerical simulations of turbulent flows present significant challenges in fluid dynamics due to their complexity and high computational cost. High resolution techniques such as Di…