Written by Sanghyuk Kim

Publications

Type of author: *Corresponding author

πŸ“– International Journal Papers

  1. Kim S, Seo M, Yang S*, Kang N* (2025) Rigid-Deformation Decomposition AI Framework for 3D Spatio-Temporal Prediction of Vehicle Collision Dynamics. (under review). https://arxiv.org/abs/2503.19712

  2. Kim S, Kim H*, Kang N, Lee TH* (2025) Projected variable three-term conjugate gradient algorithm for enhancing generalization performance in deep neural network training. Neurocomputing, 131568. https://doi.org/10.1016/j.neucom.2025.131568

  3. Kim H, Wang C, Byun H, Hu W,* Kim S, Jiao Q, Lee TH* (2023) Variable three-term conjugate gradient method for training artificial neural networks. Neural Networks 159:125–136. https://doi.org/10.1016/j.neunet.2022.12.001

πŸ—£οΈ International Conference Papers

  1. Kim S, Kim H, Kang N* (2026) Text-guided multiscale topology optimization for mechanical anisotropy design with TPMS. 17th World Congress on Computational Mechanics (WCCM) & 10th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS), July 19–24, 2026, Munich, Germany.

  2. Kim S, Yang S, Seo M, Kang N* (2025) Decoupled dynamics framework with neural fields for 3D spatio-temporal prediction of vehicle collisions. ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2025), August 17–20, 2025, Anaheim, California, Paper No. DETC2025-167614.

  3. Kim S, Kim H, Lee TH* (2023) Regularized variable three-term conjugate gradient method to improve the generalization performance of neural network training. 15th World Congress of Structural and Multidisciplinary Optimization, June 5–9, 2023, Cork, Ireland.

πŸ—£οΈ Korean Conference Papers

  1. Kim S, Kim H, Kang N* (2025) Generative AI-Driven Mixture-of-Experts Framework using Implicit Neural Representations for Additive Manufacturing. KSME Annual Conference 2025, December 10–12, 2025, Jeongseon, Korea.

  2. Kim S Han J, Kim S, Cho Y, Kang N* (2025) Manufacturing-Aware 3D Geometry Post-Processing Framework via Depth Maps for 3-Axis CNC Milling. KSME Annual Conference 2025, December 10–12, 2025, Jeongseon, Korea.

  3. Kim S, Yang S, Kim S, Han J, Kang N* (2025) Transformer-based Framework for Large-Scale 3D Flow Prediction. KSME Annual Conference 2025, December 10–12, 2025, Jeongseon, Korea.

  4. Kim S, Yang S, Kim S, Han J, Cho B, Song C, Kang J, Song M, Kang N* (2025) Prediction of Unsteady Flow Fields for Varying Multi-Cylinder Arrangements Using DeepONet. Applied Artificial Intelligence Conference (AAiCON), July 3–4, 2025, Daejeon, Korea. Outstanding paper award

  5. Han J, Kim S, Kim S, Yang S, Cho B, Song C, Kang J, Song M, Kang N* (2025) Time-Series Flow Field Prediction of Multi-Cylinder using Spatio-Temporal Coordinate-based Neural Network. KSME CAE & Applied Mechanics Division 2025 Conference, April 17–19, 2025, Jeju, Korea.

  6. Kim S, Yang S, Kim S, Han J, Cho B, Song C, Kang J, Song M, Kang N* (2025) Spatio-Temporal Flow Field Prediction of Variable Multi-Cylinder Configurations via DeepONet. KSME CAE & Applied Mechanics Division 2025 Conference, April 17–19, 2025, Jeju, Korea.

  7. Kim S, Seo M, Kang N* (2024) 3D Spatiotemporal Prediction of Vehicle Collision Dynamics via Implicit Neural Representations. KSME 2024 Conference, November 6–9, 2024, Jeju, Korea.

  8. Kim S, Seo M, Kang N* (2024) Spatiotemporal Prediction of Structures via Implicit Neural Representations. KSME CAE & Applied Mechanics Division 2024 Conference, May 1–4, 2024, Jeju, Korea.

  9. Kim S, Lee TH* (2023) Influences of Data and Network Characteristics on Optimization Algorithm and Generalization Performance. 2023 KSCM Conference on Computational Mechanics, June 29–July 1, 2023, Gangwon, Korea

  10. Lee TH, Kim S (2023) Influences of Hyperparameters at Optimization Algorithms in Artificial Neural Network Training. KSME CAE & Applied Mechanics Division 2023 Conference, May 17–20, 2023, Busan, Korea.

  11. Lee TH, Kim S, Kim H (2022) Issues on optimization algorithms in training artificial neural networks. KSME 2022 Conference, November 9–12, 2022, Jeju, Korea.

  12. Kim S, Kim H, Lee TH* (2022) Adaptive three-term conjugate gradient algorithm with stochastic noise for escaping local minima in training ANNs. KSME 2022 Conference, November 9–12, 2022, Jeju, Korea. Outstanding paper award

  13. Kim S, Kim H, Choi S, Kim G, Huh K, Lee TH* (2022) Improved adaptive three-term conjugate gradient algorithm for training artificial neural networks. KSME CAE & Applied Mechanics Division 2022 Conference, May 19–21, 2022, Busan, Korea.

🧾 Patents

  1. μ•”μ‹œμ  μ‹ κ²½ ν‘œν˜„ 기반의 동역학 예츑 방법 및 κ·ΈλŸ¬ν•œ 방법이 κ΅¬ν˜„λœ μ „μž μž₯치. KIPO Patent Application, μΆœμ›λ²ˆν˜Έ: 40-2025-0043259.