26 citations · 41 across the 4 of their papers we have counts for
5 papers
Uncovering flame physics with machine learning: application to the reaction rate in hydrogen flames
Antonio Attili, Ludovico Nista, Tommaso Baffetti +6
Convolutional Neural Networks (CNNs) are used as analytical tools to investigate the relationship between the progress variable field and the local chemical source term in lean pre…
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…
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…
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…
Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes
Temistocle Grenga, Ludovico Nista, Christoph Schumann +4
Premixed flames exhibit different asymptotic regimes of interaction between heat release and turbulence depending on their respective length scales. At high Karlovitz number, the d…