Kim, Taegeun, Hyoungsoo Ko, Jaewon Jang, Sejin Kim, Sunyoung Park, Jae Myung Choe, Young-gu Kim, Dae Sin Kim, Sangseung Lee, and Donghyun You. "An efficient multiscale coupling method for simulations of reactor-scale chemical vapor deposition with microstructural features." Advances in Engineering Software 212 (2026): 104051.

Rüttgers, Mario, Moritz Waldmann, Fabian Hübenthal, Klaus Vogt, Makoto Tsubokura, Sangseung Lee, and Andreas Lintermann. "Towards a widespread usage of computational fluid dynamics simulations for automated virtual nasal surgery planning." Future Generation Computer Systems 174 (2026): 107935.

Jang, B., Jeon, M. J., Lee, S., & Jin, H. (2025). Neural network-based thermal model for virtual metrology of lunar orbiter temperatures via active and transfer learning. International Communications in Heat and Mass Transfer, 165, 109055.

Shi, Z., Khorasani, S. M. H., Shin, H., Yang, J., Lee, S., & Bagheri, S. (2025). Drag prediction of rough-wall turbulent flow using data-driven regression. Flow, 5 E5. [arXiv link]

Shin, H., Khorasani, S. M. H., Shi, Z., Yang, J., Bagheri, S., & Lee, S. (2024). Data-driven discovery of drag-inducing elements on a rough surface through convolutional neural networks. Physics of Fluids 36 (9) [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 Combustion (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.