Main Research Interests:

• Development of data-driven closure models for non-reactive and reactive turbulent flows
• Computational fluid dynamics, high-performance computing, and deep learning.

• Arumapperuma G., Sorace N., Jansen M., Bladek O., Nista L., Sakhare S., ... & Attili A. (2025). Extrapolation Performance of Convolutional Neural Network-Based Combustion Models for Large-Eddy Simulation: Influence of Reynolds Number, Filter Kernel and Filter Size. Flow, Turbulence and Combustion, 1-30. DOI: 10.1007/s10494-025-00643-w
• Nista L., Schumann C. D. K., Petkov P., Pavlov V., Grenga T., MacArt J. F., Attili A., Stoyan M., and Pitsch H. (2025). Parallel implementation and performance of super-resolution generative adversarial network turbulence models for large-eddy simulation. Computers & Fluids, 106498. DOI: 10.1016/j.compfluid.2024.106498
• Nista L., Schumann C. D. K., Vivenzo M., Fröde F., Grenga T., MacArt J. F., Attili A., and Pitsch H. (2024). Homogeneous isotropic turbulence database for training super-resolution data-driven turbulence closure models. No. RWTH-2024-03259. Lehrstuhl und Institut für Technische Verbrennung. DOI: 10.18154/RWTH-2024-03259
• Nista L., Schumann C. D. K., Bode M., Grenga T., MacArt J. F., Attili A., and Pitsch H. (2024). Influence of adversarial training on super-resolution turbulence reconstruction. Physical Review Fluids, 9(6), 064601. DOI: 10.1103/PhysRevFluids.9.064601
• Nista L., Schumann C. D. K., Grenga T., Attili A., and Pitsch H. (2023). Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows. Proceedings of the Combustion Institute, 39(4), 5279-5288. DOI: 10.1016/j.proci.2022.07.244
• Grenga T., Nista L., Schumann C. D. K., Karimi A., Scialabba G., Attili A., and Pitsch H. (2023). Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes. In Combustion Science and Technology, 195(15), 3923-3946. DOI: 10.1080/00102202.2022.204162
• Nista L., Schumann C. D. K., Scialabba G., Grenga T., Attili A., and Pitsch H. (2022). "The influence of adversarial training on turbulence closure modeling." In AIAA Scitech 2022 Forum (p. 0185).
• Attili A., Sorace N., Nista L., Schumann C., Karimi A., Scialabba G., Grenga T., and Pitsch H. (2021). "Investigation of the Extrapolation Performance of Machine Learning Models for LES of Turbulent Premixed Combustion." In 10th European Combustion Meetings
• Grenga T., Nista L., Schumann C., Karimi A., Scialabba G., Bode M., Attili A., and Pitsch, H. (2021). "Predictive data-driven turbulent combustion model through Super Resolution Generative Adversarial Network." In 10th European Combustion Meetings
• Nista L., Schumann C., Grenga T., Karimi A., Scialabba G., Bode M., Attili A., and Pitsch, H. (2021). "Turbulent mixing predictive model with physics-based Generative Adversarial Network". In 10th European Combustion Meetings
• Ozden A., Nista L., Saracoglu, B. H. (2020). "Performance evaluations of the STRATOFLY MR3 propulsive nozzle at supersonic speeds." In AIAA Propulsion and Energy 2020 Forum (p. 3716).
• Nista L., Saracoglu B. H. (2019). "Development of a robust solver to model the flow inside the engines for high-speed propulsion." In MATEC Web of Conferences (Vol. 304, p. 03013). EDP Sciences.
• Nista L., Saracoglu B. H. (2019). "Numerical investigation of the STRATOFLY MR3 propulsive nozzle during supersonic to hypersonic transition." In AIAA Propulsion and Energy 2019 Forum (p. 3843).
• Nista L., Ispir A. C., and Saracoglu B. H. (2019). "A detailed combustion solver for detonation engines simulations." In AIAA Scitech 2019 Forum (p. 2250).
• Ispir A. C., Nista L., Saracoglu B., and Magin, T. (2019). "Detailed Chemistry Investigation of Hydrogen and Hydrocarbon Based Fuel Mixture for Detonation Engine." In AIAA Scitech 2019 Forum (p. 1502).

For a student assistant position or a thesis project, please send me an email with your CV and transcript of records. I would be happy to explain our research areas and offer you a suitable project. I am always open to working with students on exciting topics!