3 papers
physics.flu-dyn2026
A convolutional autoencoder and neural ODE framework for surrogate modeling of transient counterflow flames
Mert Yakup Baykan, Weitao Liu, Thorsten Zirwes +3
A novel convolutional autoencoder neural ODE (CAE-NODE) framework is proposed for a reduced-order model (ROM) of transient 2D counterflow flames, as an extension of AE-NODE methods…
physics.flu-dyn2025
Super-resolution of turbulent velocity fields in two-way coupled particle-laden flows
Ali Shamooni, Ruyue Cheng, Thorsten Zirwes +3
This paper introduces a deep learning-based super-resolution (SR) framework specifically developed for accurately reconstructing high-resolution velocity fields in two-way coupled…
physics.flu-dyn2025
Super-resolution of turbulent velocity and scalar fields using different scalar distributions
Ali Shamooni, Oliver T. Stein, Andreas Kronenburg
In recent years, sub-grid models for turbulent mixing have been developed by data-driven methods for large eddy simulation (LES). Super-resolution is a data-driven deconvolution te…