| Deep recurrent optical flow learning for particle image velocimetry data C Lagemann, K Lagemann, S Mukherjee, W Schröder Nature Machine Intelligence 3 (7), 641-651, 2021 | 97 | 2021 |
| Peregrine Falcon’s Dive: Pullout Maneuver and Flight Control Through Wing Morphing O Selim, ER Gowree, C Lagemann, E Talboys, C Jagadeesh, C Brücker AIAA Journal 59 (10), 3979-3987, 2021 | 34 | 2021 |
| Analysis of transonic buffet using dynamic mode decomposition A Feldhusen-Hoffmann, C Lagemann, S Loosen, P Meysonnat, M Klaas, ... Experiments in Fluids 62 (4), 1-17, 2021 | 34 | 2021 |
| Generalization of deep recurrent optical flow estimation for particle-image velocimetry data C Lagemann, K Lagemann, S Mukherjee, W Schröder Measurement Science and Technology 33 (9), 094003, 2022 | 30 | 2022 |
| Deep learning of causal structures in high dimensions under data limitations K Lagemann, C Lagemann, B Taschler, S Mukherjee Nature Machine Intelligence 5 (11), 1306-1316, 2023 | 27 | 2023 |
| Vortices enable the complex aerobatics of peregrine falcons ER Gowree, C Jagadeesh, E Talboys, C Lagemann, C Brücker Communications biology 1 (1), 27, 2018 | 23 | 2018 |
| Deep artificial neural network architectures in PIV applications C Lagemann, K Lagemann, W Schröder, M Klaas 13th International Symposium on Particle Image Velocimetry, 2019 | 14 | 2019 |
| Unsupervised Recurrent All-Pairs Field Transforms for Particle Image Velocimetry C Lagemann, M Klaas, W Schröder 14th International Symposium on Particle Image Velocimetry 1 (1), 2021 | 13 | 2021 |
| Invariance-based Learning of Latent Dynamics K Lagemann, C Lagemann, S Mukherjee The Twelfth International Conference on Learning Representations, 2023 | 10* | 2023 |
| Towards extending the aircraft flight envelope by mitigating transonic airfoil buffet E Lagemann, SL Brunton, W Schröder, C Lagemann Nature Communications 15 (1), 5020, 2024 | 9* | 2024 |
| Impact of Reynolds number on the drag reduction mechanism of spanwise travelling surface waves E Lagemann, M Albers, C Lagemann, W Schröder Flow, Turbulence and Combustion 113 (1), 27-40, 2024 | 8 | 2024 |
| Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements E Lagemann, SL Brunton, C Lagemann Proceedings of the Royal Society A 480 (2292), 20230798, 2024 | 7 | 2024 |
| Challenges of deep unsupervised optical flow estimation for particle-image velocimetry data C Lagemann, K Lagemann, S Mukherjee, W Schröder Experiments in Fluids 65 (3), 30, 2024 | 7 | 2024 |
| Analysis of PIV Images of Transonic Buffet Flow by Recurrent Deep Learning Based Optical Flow Prediction C Lagemann, E Mäteling, M Klaas, W Schröder 20th International Symposium on Applications of Laser and Imaging Techniques …, 2022 | 7 | 2022 |
| Experimental and numerical analysis of the aerodynamics of peregrine falcons during stoop flight C Lagemann, ER Gowree, C Jagadeesh, E Talboys, C Brücker Deutsche Gesellschaft für Luft-und Raumfahrt-Lilienthal-Oberth eV, 2018 | 5 | 2018 |
| Key aspects of unsupervised optical flow models in PIV applications C Lagemann, W Schröder 15th international symposium on particle image velocimetry, 2023 | 4 | 2023 |
| Instantaneous wall-shear stress distribution based on wall-normal PIV measurements using deep optical flow E Lagemann, W Schröder, C Lagemann 15th international symposium on particle image velocimetry, 2023 | 4 | 2023 |
| Dataset: Deep Recurrent Optical Flow Learning for Particle Image Velocimetry Data C Lagemann, K Lagemann, S Mukherjee, W Schröder Statistics and Machine Learning, 2021 | 3 | 2021 |
| Deep Recurrent Neural Networks for Optical Flow Learning in Particle-image Velocimetry C Lagemann Verlag Dr. Hut, 2022 | 1 | 2022 |
| Mitigating transonic buffet with porous trailing edges E Lagemann, S Brunton, W Schröder, C Lagemann Bulletin of the American Physical Society, 2024 | | 2024 |
