| Unsupervised deep learning for super-resolution reconstruction of turbulence H Kim*, J Kim*, S Won, C Lee Journal of Fluid Mechanics 910, A29, 2021 | 266 | 2021 |
| Prediction of turbulent heat transfer using convolutional neural networks J Kim, C Lee Journal of Fluid Mechanics 882, A18, 2020 | 177 | 2020 |
| Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers J Kim, C Lee Journal of Computational Physics 406, 109216, 2020 | 99 | 2020 |
| Deep reinforcement learning for large-eddy simulation modeling in wall-bounded turbulence J Kim, H Kim, J Kim, C Lee Physics of Fluids 34 (10), 105132, 2022 | 39 | 2022 |
| Interpretable deep learning for prediction of Prandtl number effect in turbulent heat transfer H Kim, J Kim, C Lee Journal of Fluid Mechanics 955, A14, 2023 | 19 | 2023 |
| Turbulence control for drag reduction through deep reinforcement learning T Lee, J Kim, C Lee Physical Review Fluids 8 (2), 024604, 2023 | 13 | 2023 |
| Prediction and control of two-dimensional decaying turbulence using generative adversarial networks J Kim, J Kim, C Lee Journal of Fluid Mechanics 981, A19, 2024 | 7 | 2024 |
| Prediction and Control of 2D Decaying Turbulence using Generative Adversarial Networks J Kim, J Kim, C Lee Bulletin of the American Physical Society 67, 2022 | | 2022 |
| Deep Reinforcement Learning for Large-Eddy Simulation Subgrid-Scale Modeling in Turbulent Channel Flow J Kim, H Kim, J Kim, C Lee Bulletin of the American Physical Society 67, 2022 | | 2022 |
| Interpretable Deep Learning for Turbulent Heat Transfer with Prandtl Number Effect H Kim, J Kim, C Lee Bulletin of the American Physical Society 67, 2022 | | 2022 |
| PREDICTION AND CONTROL OF TURBULENT FLOWS USING DEEP LEARNING J Kim, J Kim, C Lee 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022, 2022 | | 2022 |
| INTERPRETATION AND PREDICTION FOR PRANDTL NUMBER EFFECT IN TURBULENT HEAT TRANSFER USING GENERATIVE ADVERSARIAL NETWORKS H Kim, J Kim, C Lee 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022, 2022 | | 2022 |
| DEEP REINFORCEMENT LEARNING FOR LARGE-EDDY SIMULATION OF WALL-BOUNDED TURBULENCE J Kim, H Kim, J Kim, C Lee 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022, 2022 | | 2022 |
| Super-resolution reconstruction of turbulence using unsupervised deep learning H Kim, J Kim, S Won, C Lee APS Division of Fluid Dynamics Meeting Abstracts, R01. 023, 2020 | | 2020 |
| Modeling wall-shear stress of turbulent flows through deep reinforcement learning J Kim, H Kim, C Lee APS Division of Fluid Dynamics Meeting Abstracts, R01. 022, 2020 | | 2020 |
| Turbulence inflow generation using generative adversarial network J Kim, C Lee APS Division of Fluid Dynamics Meeting Abstracts, C17. 006, 2019 | | 2019 |
| Deep learning of turbulent heat transfer in channel flow J Kim, C Lee Bulletin of the American Physical Society 63, 2018 | | 2018 |
| Super-resolution reconstruction of turbulence through unsupervised deep learning H Kim, J Kim, S Won, C Lee | | |
| Turbulence modeling via deep reinforcement learning J Kim, H Kim, C Lee | | |
