| Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil S Yang, S Lee, K Yee Engineering with Computers, 2022, 2022 | 76 | 2022 |
| Data-driven physics-informed neural networks: A digital twin perspective S Yang, H Kim, Y Hong, K Yee, R Maulik, N Kang Computer Methods in Applied Mechanics and Engineering 428, 117075, 2024 | 59 | 2024 |
| Physics-aware Reduced-order Modeling of Transonic Flow via -Variational Autoencoder YE Kang*, S Yang* (*Co-1st authors), K Yee Physics of Fluids 34, 076103 (2022), 2022 | 51 | 2022 |
| Design rule extraction using multi-fidelity surrogate model for unmanned combat aerial vehicles S Yang, K Yee Journal of Aircraft 59 (4), 977-991, 2022 | 37 | 2022 |
| Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task S Yang, K Yee Engineering Applications of Artificial Intelligence 132 (107871), 2024 | 28 | 2024 |
| Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction S Yang, V Ricardo, N Kang arXiv preprint arXiv:2406.03789, 2024 | 15 | 2024 |
| Physics-constrained graph neural networks for spatio-temporal prediction of drop impact on oled display panels J Kim, J Park, N Kim, Y Yu, K Chang, CS Woo, S Yang*, ... Expert Systems with Applications 274, 126907, 2025 | 10 | 2025 |
| Exploration of stacked rotor designs for aerodynamics in hover Y Hong, D Lee, S Yang, H Kook, K Yee Aerospace Science and Technology 141, 108557, 2023 | 10 | 2023 |
| Model-agnostic AI framework with explicit time integration for long-term fluid dynamics prediction S Yang, R Vinuesa, N Kang Journal of Computational Design and Engineering 12 (10), 133-153, 2025 | 6* | 2025 |
| Comment on “novel approach for selecting low-fidelity scale factor in multifidelity metamodeling” S Yang, K Yee AIAA Journal 60 (4), 2713-2715, 2022 | 5 | 2022 |
| Data-efficient deep operator network for unsteady flow: A multi-fidelity approach with physics-guided subsampling S Yang, Y Lee, N Kang Computer Methods in Applied Mechanics and Engineering 446, 118254, 2025 | 4* | 2025 |
| Multi-fidelity modeling via regression-based hierarchical kriging S Yang, YE Kang, K Yee Asia-Pacific International Symposium on Aerospace Technology, 643-652, 2021 | 4 | 2021 |
| Leveraging Deep Neural Networks for Efficient Prediction of Aerodynamic Performance Tables YE Kang, D Lee, Y Hong, S Yang, K Yee AIAA AVIATION FORUM AND ASCEND 2024, 3754, 2024 | 3 | 2024 |
| Uncertainty Quantification via Deep Ensembles in Missile Performance Prediction S Yang, K Yee AIAA SCITECH 2023 Forum, 2684, 2023 | 3 | 2023 |
| AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble D Lee, S Yang, JW Oh, SG Cho, S Kim, N Kang arXiv preprint arXiv:2412.05475, 2024 | 2 | 2024 |
| Multi-objective Generative Design Framework and Realization for Quasi-serial Manipulator: Considering Kinematic and Dynamic Performance S Lee, S Yang, N Kang Engineering Applications of Artificial Intelligence 161 (112142), 2024 | 2 | 2024 |
| Decoupled Dynamics Framework With Neural Fields for 3D Spatio-Temporal Prediction of Vehicle Collisions S Kim, S Yang, M Seo, N Kang International Design Engineering Technical Conferences and Computers and …, 2025 | 1 | 2025 |
| Point-wise Diffusion Models for Physical Systems with Shape Variations: Application to Spatio-temporal and Large-scale system J Kim, S Yang*, N Kang* (*co-corresponding) arXiv preprint arXiv:2508.01230, 2025 | 1 | 2025 |
| Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module J Shin, C Kim, S Yang, M Lee, SJ Kim, J Jeon arXiv preprint arXiv:2504.16447, 2025 | 1 | 2025 |
| Multi-Fidelity Optimization via Regression-based Hierarchical Kriging S Yang, YE Kang, K Yee Conference of the Korean Society of Mechanical Engineers, 34-34, 2021 | 1 | 2021 |
Namwoo KangKAISTVerified email at kaist.ac.kr
