Papers matching “data-driven turbulence modeling” 18
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Predictive Data-Driven Model Based on Generative Adversarial Network for Premixed Turbulence-Combustion Regimes2022 Combustion Science and Technology article Engineering Combustion and flame dynamics Open access
Temistocle Grenga, Ludovico Nista, Christoph Schumann, Gandolfo Scialabba, Antonio Attili, Heinz G. Pitsch
15citations -
Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models2022 arXiv (Cornell University) preprint Computer Science Evolutionary Algorithms and Applications Open access
Fabian Waschkowski, Haochen Li, Abhishek J. Deshmukh, Temistocle Grenga, Yaomin Zhao, Heinz G. Pitsch, +2 more
4citations -
Data-driven modeling of resolved and filtered thermo-diffusively unstable hydrogen–air flames2024 Proceedings of the Combustion Institute article Engineering Combustion and flame dynamics Open access
Arianna Remiddi, Pasquale Eduardo Lapenna, Davide Cavalieri, Davide Schintu, Giuseppe Indelicato, Antonio Attili, +3 more
6citations -
Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models2024 Flow Turbulence and Combustion article Computer Science Evolutionary Algorithms and Applications Open access
Fabian Waschkowski, Haochen Li, Abhishek J. Deshmukh, Temistocle Grenga, Yaomin Zhao, Heinz G. Pitsch, +2 more
3citations -
Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows2021 Proceedings of the Combustion Institute article Physics and Astronomy Model Reduction and Neural Networks Open access
Mathis Bode, Michael Gauding, Zeyu Lian, Dominik Denker, Marco Davidovic, Konstantin Kleinheinz, +2 more
150citations -
Applying physics-informed enhanced super-resolution generative adversarial networks to turbulent premixed combustion and engine-like flame kernel direct numerical simulation data2023 Proceedings of the Combustion Institute article Physics and Astronomy Model Reduction and Neural Networks Open access
Mathis Bode, Michael Gauding, Dominik Goeb, Tobias Falkenstein, Heinz G. Pitsch
31citations -
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data2022 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access
Mathis Bode, Michael Gauding, Dominik Goeb, Tobias Falkenstein, Heinz G. Pitsch
0citations -
HydroGym: A Multi-Solver Reinforcement Learning Platform for Fluid Dynamics2026 Zenodo (CERN European Organization for Nuclear Research) other Physics and Astronomy Model Reduction and Neural Networks Open access
Christian Lagemann, Sajeda Mokbel, Miro Gondrum, Mario Rüttgers, Yuning Wang, Pol Suárez, +15 more
1citations -
Hybrid Inference Optimization for AI-Enhanced Turbulent Boundary Layer Simulation on Heterogeneous Systems2026 JuSER Publikationsportal article Computer Science Parallel Computing and Optimization Techniques Open access
Fabian Orland, Tom Hilgers, Fabian Hübenthal, Rakesh Sarma, Andreas Lintermann, Christian Terboven
0citations -
Control-oriented wind turbine load estimation based on local linear neuro-fuzzy models2024 Journal of Physics Conference Series conference-paper Engineering Wind Turbine Control Systems Open access
A Klein, Thorben Wintermeyer‐Kallen, J Stegink, Tarek Kösters, János Zierath, Oliver Nelles, +2 more
5citations
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