| Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows L Nista, CDK Schumann, T Grenga, A Attili, H Pitsch Proceedings of the Combustion Institute 39 (4), 5279-5288, 2023 | 12 | 2023 |
| Turbulent mixing predictive model with physics-based Generative Adversarial Network L Nista, C Schumann, T Grenga, AN Karimi, G Scialabba, M Bode, A Attili, ... 10th European combustion meeting, 460-465, 2021 | 6 | 2021 |
| Numerical investigation of the STRATOFLY MR3 propulsive nozzle during supersonic to hypersonic transition L Nista, BH Saracoglu AIAA Propulsion and Energy 2019 Forum, 3843, 2019 | 6 | 2019 |
| Investigation of the extrapolation performance of machine learning models for les of turbulent premixed combustion A Attili, N Sorace, L Nista, C Schumann, A Karimi, G Scialabba, T Grenga, ... Proceedings European combustion meeting, 349-354, 2021 | 5 | 2021 |
| Predictive Data-Driven Model Based on Generative Adversarial Network for Premixed Turbulence-Combustion Regimes T Grenga, L Nista, C Schumann, A Karimi, G Scialabba, A Attili, H Pitsch Combustion Science and Technology, 1-24, 2022 | 4 | 2022 |
| The influence of adversarial training on turbulence closure modeling L Nista, CKD Schumann, G Scialabba, T Grenga, A Attili, H Pitsch AIAA SCITECH 2022 Forum, 0185, 2022 | 4 | 2022 |
| A Detailed Combustion Solver for Detonation Engines Simulations L Nista, BH Saracoglu, AC Ispir AIAA Scitech 2019 Forum, 2250, 2019 | 3 | 2019 |
| Development of a robust solver to model the flow inside the engines for high-speed propulsion L Nista, BH Saracoglu MATEC Web of Conferences 304, 03013, 2019 | 2 | 2019 |
| Predictive data driven turbulence-combustion model through Super Resolution Generative Adversarial Network T Grenga, L Nista, C Schumann, AN Karimi, G Scialabba, M Bode, A Attili, ... 10th European combustion meeting, 426-431, 2021 | 1 | 2021 |
| Performance evaluations of the stratofly mr3 propulsive nozzle at supersonic speeds A Ozden, L Nista, BH Saracoglu AIAA Propulsion and Energy 2020 Forum, 3716, 2020 | 1 | 2020 |
| Homogeneous isotropic turbulence database for training super-resolution data-driven turbulence closure models L Nista, CDK Schumann, M Vivenzo, F Fröde, T Grenga, JF MacArt, A Attili, ... Lehrstuhl und Institut für Technische Verbrennung, 2024 | | 2024 |
| Influence of adversarial training on super-resolution turbulence models L Nista, CDK Schumann, M Bode, T Grenga, JF MacArt, A Attili, H Pitsch arXiv preprint arXiv:2308.16015, 2023 | | 2023 |
| LES models for turbulent hydrogen flames with convolutional neural networks A Attili, MGD Jansen, N Sorace, M Bruce, T Grenga, L Nista, L Berger, ... Associazione Sezione Italiana del Combustion Institute, 2023 | | 2023 |
| Detailed Chemistry Investigation of Hydrogen and Hydrocarbon Based Fuel Mixture for Detonation Engine AC Ispir, L Nista, B Saracoglu, T Magin AIAA Scitech 2019 Forum, 1502, 2019 | | 2019 |
| INFLUENCE OF ADVERSARIAL TRAINING ON SUPER-RESOLUTION TURBULENCE RECONSTRUCTION L Nista, CDK Schumann, M Bode, T Grenga, JF MacArt, A Attili, H Pitsch | | |
| Development of a reacting flow solver to model the combustion of hydrocarbons fuels in detonation engines L Nista | | |
Temistocle GrengaRWTHVerified email at itv.rwth-aachen.de
