| Stabilization of the fluidic pinball with gradient-enriched machine learning control GYC Maceda, Y Li, F Lusseyran, M Morzyński, BR Noack Journal of Fluid Mechanics 917, A42, 2021 | 61 | 2021 |
| Machine-learning flow control with few sensor feedback and measurement noise R Castellanos, GY Cornejo Maceda, I de la Fuente, BR Noack, A Ianiro, ... Physics of Fluids 34 (4), 047118, 2022 | 33 | 2022 |
| Bayesian optimization for active flow control AB Blanchard, GY Cornejo Maceda, D Fan, Y Li, Y Zhou, BR Noack, ... Acta Mechanica Sinica 37 (12), 1786-1798, 2021 | 28 | 2021 |
| Stabilization of a multi-frequency open cavity flow with gradient-enriched machine learning control GYC Maceda, E Varon, F Lusseyran, BR Noack Journal of Fluid Mechanics 955, A20, 2023 | 19 | 2023 |
| Sensitivity-aided active control of flow past twin cylinders L Zhou, H Li, KT Tim, X He, GYC Maceda, H Zhang International Journal of Mechanical Sciences 242, 108013, 2023 | 16 | 2023 |
| Cluster-based control for net drag reduction of the fluidic pinball X Wang, N Deng, GY Cornejo Maceda, BR Noack Physics of Fluids 35 (2), 2023 | 14 | 2023 |
| Topologically assisted optimization for rotor design T Wang, Y Yang, X Chen, P Li, A Iollo, GY Cornejo Maceda, BR Noack Physics of Fluids 35 (5), 2023 | 10 | 2023 |
| Artificial intelligence control applied to drag reduction of the fluidic pinball GY Cornejo Maceda, BR Noack, F Lusseyran, N Deng, L Pastur, ... PAMM 19 (1), e201900268, 2019 | 10 | 2019 |
| Aerodynamic optimization of a generic light truck under unsteady conditions using gradient-enriched machine learning control R Semaan, P Oswald, GY Cornejo Maceda, BR Noack Experiments in Fluids 64 (3), 59, 2023 | 8 | 2023 |
| xMLC--A Toolkit for Machine Learning Control GYC Maceda, F Lusseyran, BR Noack arXiv preprint arXiv:2208.13172, 2022 | 6 | 2022 |
| Real-time feedback stall control of an airfoil at large Reynolds numbers using linear genetic programming PY Passaggia, A Quansah, N Mazellier, GYC Maceda, A Kourta Physics of Fluids 34 (4), 045108, 2022 | 6 | 2022 |
| Gradient-enriched machine learning control exemplified for shear flows in simulations and experiments GY Cornejo Maceda Université Paris-Saclay, 2021 | 6 | 2021 |
| XPDT: A Toolkit for Persistent Data Topology T Wang, GYC Maceda, BR Noack Technische Universität Braunschweig, 2023 | 4 | 2023 |
| xMLC--A Toolkit for Machine Learning Control GY Cornejo Maceda, F Lusseyran, BR Noack arXiv e-prints, arXiv: 2208.13172, 2022 | 4 | 2022 |
| Towards human-interpretable, automated learning of feedback control for the mixing layer H Li, GYC Maceda, Y Li, J Tan, M Morzyński, BR Noack arXiv preprint arXiv:2008.12924, 2020 | 4 | 2020 |
| Jet mixing enhancement with Bayesian optimization, deep learning, and persistent data topology BR Noack, Y Li, T Wang, GYC Maceda, E Pickering, A Tyliszczak arXiv preprint arXiv:2311.02330, 2023 | 3* | 2023 |
| Taming the fluidic pinball with artificial intelligence control GYC Maceda, BR Noack, F Lusseyran, M Morzynski, L Pastur, N Deng European Fluid Mechanics Conference, 2018 | 3 | 2018 |
| Evolutionary Machine Learning in Control GY Cornejo Maceda, BR Noack Handbook of Evolutionary Machine Learning, 629-656, 2023 | 2 | 2023 |
| Gradient-enriched machine learning control exemplified for shear flows in simulations and experiments GYC Maceda Université Paris-Saclay, 2021 | 2 | 2021 |
| Actuation manifold from snapshot data L Marra, GYC Maceda, A Meilán-Vila, V Guerrero, S Rashwan, BR Noack, ... arXiv preprint arXiv:2403.03653, 2024 | 1 | 2024 |
Bernd R. NoackNational Talent Professor at Harbin Institute of Technology; Professor at TU BerlinVerified email at hit.edu.cn
