| Data-driven prediction of unsteady flow over a circular cylinder using deep learning S Lee, D You Journal of Fluid Mechanics 879, 217-254, 2019 | 269 | 2019 |
| Prediction of a typhoon track using a generative adversarial network and satellite images M Rüttgers, S Lee, S Jeon, D You Scientific reports 9 (1), 6057, 2019 | 120 | 2019 |
| Salient drag reduction of a heavy vehicle using modified cab-roof fairings JJ Kim, S Lee, M Kim, D You, SJ Lee Journal of Wind Engineering and Industrial Aerodynamics 164, 138-151, 2017 | 62 | 2017 |
| Prediction of laminar vortex shedding over a cylinder using deep learning S Lee, D You arXiv preprint arXiv:1712.07854, 2017 | 50 | 2017 |
| Reduction of drag in heavy vehicles with two different types of advanced side skirts BG Hwang, S Lee, EJ Lee, JJ Kim, M Kim, D You, SJ Lee Journal of Wind Engineering and Industrial Aerodynamics 155, 36-46, 2016 | 49 | 2016 |
| Deep learning-based hologram generation using a white light source T Go, S Lee, D You, SJ Lee Scientific reports 10 (1), 8977, 2020 | 26 | 2020 |
| Mechanisms of a convolutional neural network for learning three-dimensional unsteady wake flow S Lee, D You arXiv preprint arXiv:1909.06042, 2019 | 25* | 2019 |
| Predicting drag on rough surfaces by transfer learning of empirical correlations S Lee, J Yang, P Forooghi, A Stroh, S Bagheri Journal of Fluid Mechanics 933, 2022 | 21 | 2022 |
| Prediction of typhoon track and intensity using a generative adversarial network with observational and meteorological data M Rüttgers, S Jeon, S Lee, D You IEEE Access 10, 48434-48446, 2022 | 19 | 2022 |
| A conservative finite volume method for incompressible Navier–Stokes equations on locally refined nested Cartesian grids A Sifounakis, S Lee, D You Journal of Computational Physics 326, 845-861, 2016 | 14 | 2016 |
| Deep learning approach in multi-scale prediction of turbulent mixing-layer J Lee, S Lee, D You arXiv preprint arXiv:1809.07021, 2018 | 12 | 2018 |
| Typhoon track prediction using satellite images in a generative adversarial network M Rüttgers, S Lee, D You arXiv preprint arXiv:1808.05382, 2018 | 10 | 2018 |
| Neural networks for improving wind power efficiency: A review H Shin, M Rüttgers, S Lee Fluids 7 (12), 367, 2022 | 8 | 2022 |
| Effects of spatiotemporal correlations in wind data on neural network-based wind predictions H Shin, M Rüttgers, S Lee Energy 279, 128068, 2023 | 7 | 2023 |
| Prediction of typhoon tracks using a generative adversarial network with observational and meteorological data M Rüttgers, S Lee, D You arXiv preprint arXiv:1812.01943, 2018 | 7 | 2018 |
| Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness – a data-driven approach J Yang, A Stroh, S Lee, S Bagheri, B Frohnapfel, P Forooghi Journal of Fluid Mechanics 975, A34, 2023 | 4 | 2023 |
| Prediction of molten steel flow in a tundish with water model data using a generative neural network with different clip sizes B Choi, S Lee, D You Journal of Mechanical Science and Technology 36 (2), 749-759, 2022 | 3 | 2022 |
| Effects of a moving weir on tundish flow during continuous-casting grade-transition S Jeon, S Lee, S Ha, S Kim, D You Journal of Mechanical Science and Technology 35 (9), 4001-4009, 2021 | 2 | 2021 |
| How Regional Wind Characteristics Affect CNN-based wind predictions: Insights from Spatiotemporal Correlation Analysis H Shin, M Rüttgers, S Lee arXiv preprint arXiv:2304.01545, 2023 | | 2023 |
| Deep learning of unsteady laminar flow over a cylinder S Lee, D You APS Division of Fluid Dynamics Meeting Abstracts, E31. 006, 2017 | | 2017 |
Mario RüttgersPhD Student, RWTH Aachen University, FZ Jülich, JARA CSDVerifierad e-postadress på aia.rwth-aachen.de
