• Shin, H., Khorasani, S. M. H., Shi, Z., Yang, J., Lee, S., & Bagheri, S. (2024). Data-driven discovery of drag-inducing elements on a rough surface through convolutional neural networks. Submitted. arXiv preprint arXiv:2405.09071. [arXiv link]

• Shi, Z., Khorasani, S. M. H., Shin, H., Yang, J., Lee, S., & Bagheri, S. (2024). Drag prediction of rough-wall turbulent flow using data-driven regression. Submitted. arXiv preprint arXiv:2405.09256. [arXiv link]

• Yang, J., Stroh, A., Lee, S., Bagheri, S., Frohnapfel, B. and Forooghi, P. Assessment of Roughness Characterization Methods for Data-Driven Predictions. Flow Turbulence Combust (2024). 

• Yang, J., Stroh, A., Lee, S., Bagheri, S., Frohnapfel, B. and Forooghi, P., 2023. Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness--a data driven approach. Journal of Fluid Mechanics, 975, A34.
  Related arXiv paper (2023): Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness--a data driven approach. arXiv preprint arXiv:2304.08958. [arXiv link]

• Shin, H., Rüttgers, M., and Lee, S., 2023. Effects of spatiotemporal correlations in wind data on neural network based wind predictions. Energy, 128068.
  Related arXiv paper (2023): How Regional Wind Characteristics Affect CNN-based wind predictions: Insights from Spatiotemporal Correlation Analysis. arXiv preprint arXiv:2304.01545. [arXiv link] 

• Shin, H., Rüttgers, M., and Lee, S., 2022. Neural networks for improving wind power efficiency: a review. Fluids, 7(12), 367.

• Rüttgers, M.,  Jeon, S., Lee, S. and You, D., 2022. Prediction of a typhoon track and intensity using a generative adversarial network with observational and meteorological data. IEEE Access, 10, pp.48434-48446.

• Choi, B., Lee, S. and You, D., 2022. Prediction of molten steel flow in a tundish with water model data using a generative neural network with different clip sizes.  Journal of Mechanical Science and Technology, 36, pp.749-759.

• Lee, S., Yang,  J., Forooghi,  P., Stroh,  A. and Bagheri, S. , 2022. Predicting drag on rough surfaces by transfer learning of empirical correlations. Journal of Fluid Mechanics, 933, A18.
  Related arXiv paper (2021): Predicting drag on rough surfaces by transfer learning of empirical correlations. arXiv:2106.05995 [arXiv link] ; Related Code: [Download link]

• Jeon, S., Lee, S., Ha, S., Kim, S. and You, D., 2021. Effects of a moving weir on tundish flow during continuous-casting grade-transition. Journal of Mechanical Science and Technology, 35, pp.4001-4009.

• Lee, S. and You, D., 2021. Analysis of a convolutional neural network for predicting unsteady volume wake flow fields. Physics of Fluids, 33, 035152.
  Related arXiv paper (2019): Mechanisms of a convolutional neural network for learning three-dimensional unsteady wake flow. arXiv preprint arXiv:1909.06042. [arXiv link] 

• Go, T., Lee, S., You, D. and Lee S.J., 2020. Deep learning-based hologram generation using a white light source. Scientific Reports, 10, 8977.

• Lee, S. and You, D., 2019. Data-driven prediction of unsteady flow over a circular cylinder using deep learning. Journal of Fluid Mechanics, 879, pp.217-254. [Top 10th most cited paper among 1,942 papers in JFM (JCR 2021 report)]
  Related arXiv paper 1 (2018): Data-driven prediction of unsteady flow fields over a circular cylinder using deep learning. arXiv preprint arXiv:1804.06076. [arXiv link]
  Related arXiv paper 2 (2017): Prediction of laminar vortex shedding over a cylinder using deep learning. arXiv preprint arXiv:1712.07854. [arXiv link] 

• Rüttgers, M., Lee, S., Jeon, S. and You, D., 2019. Prediction of a typhoon track using a generative adversarial network and satellite images. Scientific Reports, 9, 6057.
  Related arXiv paper 1 (2018): Typhoon track prediction using satellite images in a Generative Adversarial Network. arXiv preprint arXiv:1808.05382.
  Related arXiv paper 2 (2018): Prediction of typhoon tracks using a generative adversarial network with observational and meteorological data. arXiv preprint arXiv:1812.01943.

• Kim, J. J., Lee, S., Kim, M., You, D. and Lee, S. J., 2017. Salient drag reduction of a heavy vehicle using modified cab-roof fairings. Journal of Wind Engineering and Industrial Aerodynamics, 164, pp.138-151.

• Sifounakis, A., Lee, S. and You, D., 2016. A conservative finite volume method for incompressible navier–stokes equations on locally refined nested cartesian grids. Journal of Computational Physics, 326, pp.845-861.

• Hwang, B. G., Lee, S., Lee, E. J., Kim, J. J., Kim, M., You, D. and Lee, S. J., 2016. Reduction of drag in heavy vehicles with two different types of advanced side skirts. Journal of Wind Engineering and Industrial Aerodynamics, 155, pp.36-46.

• Lee, S., Kim, M., You, D., Kim, J. J. and Lee, S. J., 2016. Coarse grid large-eddy simulation of flow over a heavy vehicle. Journal of Computational Fluids Engineering, 21(1), pp.30-35.

Invited Talks

• Lee, S., 2023. AI-assisted fluid engineering for sustainable future. Seminar at Soongsil University, April 4, Seoul, South Korea.

• Lee, S., 2021. Predicting drag on rough surfaces by transfer learning of empirical correlations. ISTM seminar at Karlsruhe Institute of Technology (KIT), December 17, Karlsruhe, Germany.

• Lee, S., 2021. Predicting drag on rough surfaces by transfer learning of empirical correlations. BK21 Seminar at Pohang University of Science and Technology (POSTECH), July 30, Pohang, Korea.

• Lee, S., 2021. Data-driven prediction of unsteady wake flow using convolutional neural networks. FLOW Seminar at FLOW Centre, March 4, Stockholm, Sweden.

Conference Presentations

• Shi, Z., Khorasani, S., Shin, H., Lee, S., Bagheri, S., 2023. Towards learning drag prediction via data-driven methods,SIAM Conference on Computational Science and Engineering (CSE23), Amsterdam, Netherlands.

• Yang, J., Lee, S., Bagheri, S., Stroh, A. and Forooghi, P., 2022. Predicting roughness-induced drag based on active learning, 14th European Fluid Mechanics Conference, September, Athens, Greece.

• Lee, S., Khorasani, S., Shi, Z., Bagheri, S., 2022. Towards a data-driven analysis of drag inducing irregular roughness structures, 14th European Fluid Mechanics Conference, September, Athens, Greece.

• Shin, H., Rüttgers, M., Lee, S.  Analysis of the effect of spatial information of wind speed data on the increase in wind power generation efficiency. Winter Meeting of Korean Society for Fluid Machinery, December, Jeju, South Korea.

• Lee, S., Yang, J., Forooghi, P., Stroh, A. and Bagheri, S., 2021. Predicting drag on rough surfaces by transfer learning of empirical correlations. 74th Annual Meeting of the Division of Fluid Dynamics, American Physical Society, November 21-23, Phoenix, Arizona, USA.

• Lee, S., Yang, J., Forooghi, P., Stroh, A. and Bagheri, S., 2021.  A transfer learning framework to learn the Hama roughness function from a small dataset and empirical correlations. Euromech colloquium 614, June 16-18, Paris, France.

• Yang,  J., Lee, S., Bagheri,  S. , Stroh, A. and Forooghi, P. 2021. Towards an active learning-based model for prediction of roughness hydrodynamic properties. Euromech colloquium 614, June 16-18, Paris, France.

• Lee, S. and You, D., 2019. Mechanisms of convolutional neural networks for learning three-dimensional unsteady wake flow. 72nd Annual Meeting of the Division of Fluid Dynamics, American Physical Society, November 23-26, Seattle, Washington, USA.

• Lee, S. and You, D., 2019. Mechanisms of a convolutional neural network for learning three-dimensional unsteady wake flow. Annual Meeting of the Korean Society of Mechanical Engineers, November 13-16, Jeju, Korea.

• Lee, S. and You, D., 2018. Data-driven prediction of unsteady flow over a circular cylinder using deep learning. 71th Annual Meeting of the Division of Fluid Dynamics, American Physical Society, November 18-20, Atlanta, Georgia, USA.

• Rüttgers, M., Lee, S. and You, D., 2018. Typhoon track prediction using a generative adversarial network (GAN). 10th National Congress on Fluids Engineering, The Korean Society for Aeronautical and Space Sciences, August 22-24, Yeosu, Korea.

• Lee, S., Rüttgers, M., Jeon, S. and You, D., 2018. Deep learning prediction of unsteady flow over a cylinder using generative adversarial networks. Spring Meeting of the Korean Society for Computational Fluids Engineering, May 2-4, Jeju, Korea.

• Lee, S., Rüttgers, M., Jeon, S. and You, D., 2018. Deep learning prediction of unsteady flow using generative adversarial networks. Proceedings of the KSME Fluid Engineering Division, The Korean Society of Mechanical Engineers, April 19-20, Ulsan, Korea.

• Lee, S. and You, D., 2017. Deep learning of unsteady laminar flow over a cylinder. 70th annual meeting of the division of fluid dynamics, American Physical Society, November 19-21, Denver, Colorado, USA.

• Lee, S. and You, D., 2016. A conservative finite volume method for incompressible navier-stokes equations on locally refined nested cartesian grids. Fall Meeting of the Korean Society for Computational Fluids Engineering, November 10-11, Busan, Korea.

• Kim, M., Lee, S., You, D., Hwang, B. G., Lee, E. J., Kim, J. J. and Lee, S. J., 2016. Reduction of aerodynamic drag over a heavy vehicle using side skirts. Second International Conference in Numerical and Experimental Aerodynamics of Road Vehicles and Trains (AEROVEHICLES 2), June 21-23, Göteborg, Sweden.

• Lee, S., Kim, M. and You, D., 2015. Large-eddy simulation of turbulent flow over a heavy vehicle with drag reduction devices. 68th Annual Meeting of the Division of Fluid Dynamics, American Physical Society, November 22-24, Boston, Massachusetts, USA.

• Kim, J. J., Lee, S., You, D. and Lee, S. J., 2015. A new design concept of cab-roof fairing for drag reduction of heavy vehicle. Fall Meeting of the Korean Society of Automotive Engineers, November 18-21, Gyeongju, Korea.

• Lee, S., Kim, M. and You, D., 2015, July. Large-eddy simulation of turbulent flow over a heavy vehicle with drag reduction devices. ASME/JSME/KSME 2015 Joint Fluids Engineering Conference, July 26-31, Seoul, Korea.

• Kim, J. J., Lee, S., You, D. and Lee, S. J., 2015. Drag reduction effect of cab-roof fairing of a heavy vehicle. 18th Annual Meeting, The Wind Engineering Institute of Korea, May 29, Busan, Korea.