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physics.flu-dyn2025
Dynamic mixed turbulence modeling using a super-resolution generative adversarial approach
Ludovico Nista, Christoph D. K. Schumann, Temistocle Grenga +3
A dynamic mixed super-resolution model (DMSRM) for large-eddy simulations (LESs) is proposed, which combines the traditional dynamic mixed model (DMM) formulation with the generati…
physics.flu-dyn2025
ZipGAN: Super-Resolution-based Generative Adversarial Network Framework for Data Compression of Direct Numerical Simulations
Ludovico Nista, Christoph D. K. Schumann, Fabian Fröde +5
The advancement of high-performance computing has enabled the generation of large direct numerical simulation (DNS) datasets of turbulent flows, driving the need for efficient comp…
physics.flu-dyn2024
Influence of adversarial training on super-resolution turbulence reconstruction
Ludovico Nista, Christoph David Karl Schumann, Mathis Bode +4
Supervised super-resolution deep convolutional neural networks (CNNs) have gained significant attention for their potential in reconstructing velocity and scalar fields in turbulen…