CONFERENCES

13. J. Shin, M. Lee, C. Kim, S. Yang, J. Jeon, “A Feasibility Study of Physics-Informed Neural Network-Based Severe Accident Analysis Code”, NURETH-21, 2025 – oral.

12. J. Jeon, M. Lee, M. Song, B. Cho, S. Yang, N. Kang, H. Choi, Y. Yu, “Preliminary CFD Benchmarking of Machine Learning Algorithms for Korean Virtual Small Modular Reactor”, NURETH-21, 2025 – oral.

11. S. Kim, S. Yang, M. Seo, and N. Kang, “Decoupled Dynamics Framework with Neural Fields for 3D Spatio-Temporal Prediction of Vehicle Collisions”, ASME IDETC/CIE, 2025 – oral.

10. S. Yang, Y. Wang, A. Vishwasrao, R. Vinuesa, and N. Kang, “Integration of Temporal Dynamics in Graph U-Nets for Improved Mesh-Agnostic Spatio-Temporal Flow Prediction”, APS DFD, 2024 – Interact session.

9. S. Yang, R. Vinuesa, and N. Kang, “Mesh-agnostic Spatio-temporal Prediction of Flows using Improved Graph U-nets”, ICTAM, 2024 – oral.

8. Y.E., Kang, K. Lee, Y. Hong, S. Yang, and K. Yee, “Leveraging Deep Neural Networks for Efficient Prediction of Aerodynamic Performance Tables”, AIAA Aviation, 2024 – oral.

7. Y. Hong, D. Lee, S. Yang, H. Kook, and K. Yee, “Design Exploration for Aerodynamic Performance of Hovering Stacked Rotor”, 79th VFS Forum, 2023 – oral.

6. S. Yang, K. Yee, “Uncertainty Quantification via Deep Ensembles in Missile Performance Prediction”, AIAA SciTech, 2023  – oral. Session Chair

5. S. Yang, K. Yee, “Uncertainty Quantification via Deep Ensembles”, Asian Computational Fluid Dynamics Conference, 2022  – oral.

4. Y. Hong, D. Lee, S. Yang, and K. Yee, “Design Exploration on Aerodynamic Performance for Co-rotating Coaxial Rotor”, Asian Computational Fluid Dynamics Conference, 2022 – oral.

3. Y. Hong, D. Lee, S. Yang, and K. Yee, “Numerical Investigation and Design Exploration on Aerodynamic Performance for Stacked Rotor", 48th European Rotorcraft Forum, 2022 – oral.

2. S. Yang, K. Yee, “Quantifying Calibrated Uncertainty in Missile Aerodynamic Data via Deep Ensembles”, Asia-Pacific International Symposium on Aerospace Technology, 2022 – oral.

1. S. Yang, Y.E. Kang, and K. Yee, “Multi-fidelity optimization via regression-based hierarchical Kriging”, Asia-Pacific International Symposium on Aerospace Technology, 2021 – oral.

39. S. Kim, S. Yang, S. Kim, J. Han, B. Cho, C. Song, J. Kang, N.W. Kang, “Prediction of Unsteady Flow Fields for Varying Multi-Cylinder Arrangements Using DeepONet ”, AAICON, 2025 – oral. Best paper award

38. S. Yang, Y. Lee, N.W. Kang, “Physics-Guided Multi-Fidelity DeepONet for Data-Efficient Flow Field Prediction”, The Korean Society of Mechanical Engineers, 2025 – oral.

37. S. Kim, S. Yang, S. Kim, J. Han, B. Cho, C. Song, J. Kang, N.W. Kang, “Spatio-Temporal Flow Field Prediction of Variable Multi-Cylinder Configurations via DeepONet”, The Korean Society of Mechanical Engineers, 2025 – oral.

36. J. Han, S. Kim, S. Kim, S. Yang, J. Han, B. Cho, C. Song, J. Kang, N.W. Kang, “Time-Series Flow Field Prediction of Multi-Cylinder using Spatio-Temporal Coordinate-based Neural Network”, The Korean Society of Mechanical Engineers, 2025 – poster.

35. J. Kim, S. Yang, N.W. Kang, “Point-based Diffusion Model for Predicting 2D Spatio-Temporal and 3D Large-Scale Physical Systems with Shape Variations”, The Korean Society of Mechanical Engineers, 2025 – oral.

34. S. Yang, Y. Wang, A. Vishwasrao, R. Vinuesa, N.W. Kang, “Improved Temporal Prediction of Transient Flow Fields using Graph Neural Networks”, The Korean Society of Mechanical Engineers, 2024 – oral.

33. J. Kim, S. Yang, N.W. Kang, “Comparison of Spatio-Temporal Prediction Performance of 2D Cylinder Flow: Meshgraphnet vs Neural Implicit Representation”, The Korean Society of Mechanical Engineers, 2024 – poster.

32. S. Kim, S. Yang, N.W. Kang, “Improvement and Optimization of Ensemble-based Multi-Fidelity Approaches”, The Korean Society of Mechanical Engineers, 2024 – poster.

31. S. Yang, R. Vinuesa, N.W. Kang, “Prediction of Vortex Shedding from a Circular Cylinder using Graph Neural Networks”, Korean Society for Computational Fluids Engineering, 2024 – oral.

30. S. Yang, R. Vinuesa, N.W. Kang, “Temporal Flow Fields Prediction using Mesh-agnostic Graph U-Nets”, The Korean Society of Mechanical Engineers, 2024 – oral.

29. S. Kim, S. Yang, N.W. Kang, “Feasibility Study of Multi-fidelity LSTM for Time-series Prediction”, The Korean Society of Mechanical Engineers, 2024 – poster.

28. J. Kim, J. Park, S. Yang, N.W. Kang, “Physics-Informed Graph Neural Network Based Surrogate Model for Predicting the Durability of OLED Displays under Drop Impact”, The Korean Society of Mechanical Engineers, 2024 – oral.

27. D. Lee, S. Yang, J.W. Oh, S.G. Cho, S. Kim, N.W. Kang, “Digital Twin for Wave Energy Converter: Uncertainty Quantification of Real-time Wave Height Prediction using Deep Learning”, The Korean Society of Mechanical Engineers, 2024 – oral.

26. S. Lee, S. Yang, N.W. Kang, “Manipulator Mechanism Design Optimization using Surrogate Model-based Optimization and Sensitivity Analysis for Design Rule Extraction Methodology”, The Korean Society of Mechanical Engineers, 2024 – oral. Best paper award

25. S. Yang, H.J. Kim, Y.P. Hong, K. Yee, M. Romit, N.W. Kang, “Data-Driven Physics-Informed Neural Networks for Digital Twins”, The Korean Society for Aeronautical and Space Sciences, 2024 – oral.

24. Y. Seo, S. Lee, S. Lee, S. Yang, D. Lee, K. Yee, “Input Variable Analysis of Structural Load on Helicopter Rotor System Components via Data Mining”, The Korean Society for Aeronautical and Space Sciences, 2023 – oral.

23. S. Yang, H.J. Kim, Y.P. Hong, K. Yee, N.W. Kang, “Fluid-oriented Physics-informed Neural Networks via Adaptive Sampling and Data-driven Approaches”, The Korean Society of Mechanical Engineers, 2023 – oral. Best paper award

22. S. Yang, H.J. Kim, Y.P. Hong, K. Yee, N.W. Kang, “Prediction of 2D Flow Field using Vorticity-pressure-aware Physics-informed Neural Networks”, Korean Society for Computational Fluids Engineering, 2023 – oral.
21. Y.P. Hong, D. Lee, S. Yang, H.J. Kook, K. Yee, “Investigation of Steady and Unsteady effects of Hovering Stacked Rotor”, Korean Society for Computational Fluids Engineering, 2023 – oral.
20. S. Yang, Y.E. Kang, K. Yee, “Physics-aware prediction of high-dimensional data: theoretical background”, The Korean Society for Aeronautical and Space Sciences, 2022 – oral

19. Y.E. Kang, S. Yang, K. Yee, “Physics-aware prediction of high-dimensional data: practical applications” The Korean Society for Aeronautical and Space Sciences, 2022 – oral.

18. S. Yang, K. Yee, “Towards Quantifying Calibrated Uncertainty in Missile Performance Regression Tasks via Deep Ensembles”, The Korean Society for Aeronautical and Space Sciences, 2022 – poster.

17. S. Yang, Y.E. Kang, K. Yee, “Physics-aware prediction of high-dimensional data: theoretical background”, The Korean Society of Mechanical Engineers, 2022 – oral.

16. Y.E. Kang, S. Yang, K. Yee, “Physics-aware prediction of high-dimensional data: practical applications” The Korean Society of Mechanical Engineers, 2022 – oral.

15. S. Yang, Y.E. Kang, K. Yee, “Extraction of Physical Generating Factors from Given Dataset” AIIS Retreat, 2022 – poster.

14. S. Yang, Y.E. Kang, K. Yee, “Physics-aware Reduced-order Modeling of Transonic Flow via β-variational Autoencoder”, Korea Data 

Mining Society, 2022 – oral.

13. K. Kang, Y. Kim, E. Kang, J. Huh, S. Yang, K. Yee, “Comparison of fusion methods for modeling missile aerodynamic database based 

on multi-fidelity aerodynamic data”, Korea Institute of Military Science and Technology, 2022 – oral.

12. S. Yang, K. Yee, “Application of deep ensembles to quantifying predictive uncertainty in aerospace engineering”, The Korean Society 

of Mechanical Engineers, 2022 – oral. Best paper award

11. S. Yang, K. Yee, “Application of deep ensembles to quantifying predictive uncertainty in aerodynamic data”, The Korean Society for 

Aeronautical and Space Sciences, 2022 – poster.

10. Y.E. Kang, S. Yang, K. Yee, “Investigation on beta-VAE based reduced-order modeling of transonic flowfield”, The Korean Society for 

Aeronautical and Space Sciences, 2022 – poster.

9. S. Yang, S. Yoo, S. Jeong, K. Yee, “Missile performance prediction via multi-fidelity modelling”, The Korean Society for Aeronautical 

and Space Sciences, 2021 – oral.

8. S. Yang, S. Lee, K. Yee, “Inverse design optimization framework using variational autoencoder: application to wind turbine airfoil 

optimization”, The Korean Society for Aeronautical and Space Sciences, 2021 – oral.

7. S. Yang, Y.E. Kang, K. Yee, “Multi-fidelity optimization via regression-based hierarchical Kriging”, The Korean Society of Mechanical 

Engineers, 2021 – oral.

6. S. Yang, S. Lee, K. Yee, “Development of inverse design framework using deep generative model”, Korean Institute of Intelligent 

Systems, 2021 – oral.

5. S. Shin, S. Yang, S. Lee, K. Yee, “Airfoil inverse design performance comparison among MLP, CNN, and RNN”, The Korean Society of 

Mechanical Engineers, 2020 – oral. Best paper award

4. S. Yang, Y. Hong, S. Park, K. Yee, “Airfoil optimization of UCAV considering cruise flight performance and low-speed longitudinal 

stability”, The Korean Society for Aeronautical and Space Sciences, 2020 – poster.

3. S. Yang, S. Park, K. Yee, “Multi-objective and multivariate aerodynamic design analysis of double delta wing UCAV”, Korean Society 

for Computational Fluids Engineering, 2020 – oral.

2. S. Yang, K. Yee, “Trade-off study of the multi-objective unmanned combat aerial vehicle optimization via variable-fidelity modeling and 

data mining”, The National Congress of Fluids Engineering, 2020 – oral.

1. S. Yang, Y. Hong, S. Park, K. Yee, “Multi-objective multi-fidelity design optimization of cranked wing type UCAV considering longitudinal    stability”, The Korean Society for Aeronautical and Space Sciences, 2019 – poster.