| Multi-objective CFD-driven development of coupled turbulence closure models F Waschkowski, Y Zhao, R Sandberg, J Klewicki Journal of Computational Physics 452, 110922, 2022 | 50 | 2022 |
| Transition modeling for low pressure turbines using computational fluid dynamics driven machine learning HD Akolekar, F Waschkowski, Y Zhao, R Pacciani, RD Sandberg Energies 14 (15), 4680, 2021 | 39 | 2021 |
| Toward more general turbulence models via multicase computational-fluid-dynamics-driven training Y Fang, Y Zhao, F Waschkowski, ASH Ooi, RD Sandberg AIAA Journal 61 (5), 2100-2115, 2023 | 27 | 2023 |
| Towards robust and accurate Reynolds-averaged closures for natural convection via multi-objective CFD-driven machine learning X Xu, F Waschkowski, ASH Ooi, RD Sandberg International Journal of Heat and Mass Transfer 187, 122557, 2022 | 27 | 2022 |
| A coupled framework for symbolic turbulence models from deep-learning C Lav, AJ Banko, F Waschkowski, Y Zhao, CJ Elkins, JK Eaton, ... International Journal of Heat and Fluid Flow 101, 109140, 2023 | 8 | 2023 |
| Multi-Objective Development of Machine-Learnt Closures for Fully Integrated Transition and Wake Mixing Predictions in Low Pressure Turbines HD Akolekar, F Waschkowski, R Pacciani, Y Zhao, RD Sandberg Turbo Expo: Power for Land, Sea, and Air 86113, V10CT32A013, 2022 | 8 | 2022 |
| Gradient information and regularization for gene expression programming to develop data-driven physics closure models F Waschkowski, H Li, A Deshmukh, T Grenga, Y Zhao, H Pitsch, ... Flow, Turbulence and Combustion, 1-31, 2024 | 3 | 2024 |
| Turbulence Model Development based on a Novel Method Combining Gene Expression Programming with an Artificial Neural Network H Li, F Waschkowski, Y Zhao, RD Sandberg arXiv preprint arXiv:2301.07293, 2023 | 3 | 2023 |
| Evolutionary neural networks for learning turbulence closure models with explicit expressions H Li, Y Zhao, F Waschkowski, RD Sandberg Physics of Fluids 36 (5), 2024 | 1 | 2024 |
