SciML & UQ @ ME Purdue Univ.
BongSeok Kim
Scientific Machine Learning and Computational Science
School of Mechanical Engineering · Purdue University
BongSeok Kim
BongSeok Kim

BongSeok Kim

Ph.D. Student
School of Mechanical Engineering
Purdue University
Research Areas
Scientific Machine Learning
Multiscale Physics
Uncertainty Quantification
Computational Mechanics

Email: kim4853@purdue.edu
Affiliation:
School of Mechanical Engineering
Purdue University
Office: ME 3016
585 Purdue Mall
West Lafayette, IN 47907, USA
Email Google Scholar GitHub

Biography

BongSeok Kim is a Ph.D. student in the School of Mechanical Engineering at Purdue University, advised by Prof. Guang Lin in the Department of Mathematics and the School of Mechanical Engineering, and Prof. Li Qiao in the School of Aeronautics and Astronautics.

His research lies at the intersection of machine learning, computational science, and applied mathematics, with a focus on developing methodologies for efficient, reliable, and scalable scientific computation. His current research spans scientific machine learning, multiscale physics, uncertainty quantification, and computational mechanics. He is also interested in the mathematical foundations of scientific computing, particularly properties such as entropy stability, hyperbolicity, and conservation, and in incorporating these structures into learning algorithms. More broadly, he is interested in how advances in AI and high-performance computing can enable new approaches to large-scale simulation, scientific discovery, digital twins, and computational modeling of complex physical systems.

Education

  • Ph.D. Student, School of Mechanical Engineering, Purdue University, USA (2025–Present)
  • M.S., Department of Aerospace Engineering, Pusan National University, Republic of Korea
  • B.S., Department of Aerospace Engineering, Pusan National University, Republic of Korea

Research Interests

  • Scientific Machine Learning
  • Machine Learning for Partial Differential Equations
  • Data-Driven Physics Discovery
  • Uncertainty Quantification
  • Computational Multiscale Physics & Mechanics

Publications: (* denotes corresponding author)

  1. BongSeok Kim, Jiahao Zhang, and Guang Lin*, “Weak-form evolutionary Kolmogorov–Arnold Networks for solving partial differential equations,” Computer Methods in Applied Mechanics and Engineering, 458, 119065, 2026 https://doi.org/10.1016/j.cma.2026.119065
  2. BongSeok Kim, Shinseong Kang, and Kyunghoon Lee*, “Component-based wing structure reconfiguration and analysis on the fly,” Aerospace Science and Technology, 151, 109238. https://doi.org/10.1016/j.ast.2024.109238
  3. Suman Chakraborty, BongSeok Kim (co-1st author), and Li Qiao*, “Molecular Dynamics Investigation of Mass Transport during Evaporation for the Binary System of n-Dodecane and Nitrogen,” The Journal of Physical Chemistry B, 1520-6106, 2026 https://doi.org/10.1021/acs.jpcb.6c00493
  4. BongSeok Kim, Heesung Lee and Kyunghoon Lee*, “Structural Design Optimization of a Launch Vehicle Propellant Tank using Kriging,” Journal of the Korean Society for Aeronautical & Space Sciences, 336, 120196. 10.5139/JKSAS.2025.53.10.1027
  5. Dayoung Kang, Shinseong Kang, BongSeok Kim and Kyunghoon Lee*, “Condition-based fatigue life monitoring of a high-pressure hydrogen storage vessel using a reduced basis digital twin,” Engineering Structures, 336, 120196. https://doi.org/10.1016/j.engstruct.2025.120196
  6. BongSeok Kim, Johannes Krotz, Dinshaw S. Balsara, Ryan G. McClarren, Jiahao Zhang, and Guang Lin*, “A Hyperbolic Neural Closure for M1 Radiation Transfer,” Computer Methods in Applied Mechanics and Engineering, Under Review (July 2026). arXiv:2607.10364
  7. BongSeok Kim, Jiahao Zhang, and Guang Lin*, “BEKAN: Boundary condition-guaranteed evolutionary Kolmogorov–Arnold networks with radial basis functions for solving PDE problems,” Mathematics and Computers in Simulation, Under Review (July 2026). arXiv:2510.03576
  8. Amirhossein Mollaali, BongSeok Kim, Christian Moya, and Guang Lin*, “pADAM: A Plug-and-Play All-in-One Diffusion Architecture for Multi-Physics Learning,” Nature npj Artificial Intelligence, Under Revision (July 2026). arXiv:2603.16757
  9. BongSeok Kim, Haoyang Zhang, and Guang Lin*, “Bayesian Multi-fidelity Laplace Neural Operators for Parametric Oscillatory Partial Differential Equations,” Journal of Computational Physics, Under review
  10. BongSeok Kim, Suman Chakraborty, Gary Huang, Mehek Mathur, and Li Qiao*, “Symbolic Machine Learning for Vapor–Liquid Equilibrium Prediction in Cx–N2 Binary Mixtures,” Machine Learning with Applications, Under revision https://arxiv.org/abs/2608.11255

    11. Preprints

  11. BongSeok Kim, Jiahao Zhang, and Guang Lin*, “Lego-Diffusion model for multiphysics operator learning”
  12. BongSeok Kim, Jiahao Zhang, and Guang Lin*, “A Hyperbolic Neural Closure for Moment Systems of the Boltzmann Transport Equation”
  13. BongSeok Kim, Suman Chakraborty, Guang Lin*, Li Qiao* “MD-infored Operator Learning of Interfacial Structure for Vapor–Liquid Equilibrium”

Conferences

  1. BongSeok Kim, Johannes Krotz, Dinshaw S. Balsara, Ryan G. McClarren, Jiahao Zhang, and Guang Lin, “Symmetrizable Hyperbolic Neural Closure for M1 Radiation Transfer,” SIAM Annual Meeting 2026, Cleveland, Ohio, USA, July 2026. Oral Presentation.
  2. Suman Chakraborty, BongSeok Kim, and Li Qiao, “Molecular Dynamics Investigation of Transcritical Mass Transport during Evaporation of n-Dodecane in Nitrogen,” International Conference of Fluid Dynamics 2025, Sendai, Japan, November 2025. Oral Presentation.
  3. BongSeok Kim, Shinseon Kang, and Kyunghoon Lee, “Component-Based Aircraft Wing Reconfiguration and Structural Response Prediction on the Fly,” Asian Congress of Structural and Multidisciplinary Optimization, May 2024. Oral Presentation.
  4. Dayoung Kang, BongSeok Kim, and Kyunghoon Lee, “Reliability-based Design Optimization of a Hydrogen Storage Vessel Using Rapid yet Accurate Structural Analysis,” The Society for Aerospace System Engineering 2024 Spring Conference, May 2024.
  5. BongSeok Kim, Shinseon Kang, and Kyunghoon Lee, “Static condensation reduced basis element analysis of an aircraft wing structure with applications,” The Korean Institute of Military Science and Technology 2023 Spring Conference, June 2023.
  6. BongSeok Kim, Shinseon Kang, and Kyunghoon Lee, “Linear elastostatic analysis of a wing structure via static condensation reduced basis element method,” Korean Society for Industrial and Applied Mathematics 2023 Spring Conference, May 2023.
  7. BongSeok Kim, Shinseon Kang, and Kyunghoon Lee, “Rapid yet accurate linear elastostatic analysis of an aircraft wing structure and its applications,” The Society for Aerospace System Engineering 2023 Spring Conference, May 2023.
  8. BongSeok Kim, Shinseon Kang, and Kyunghoon Lee, “Rapid and accurate large-scale structural analysis by a static condensation reduced basis element method: application to aircraft wing structural analysis,” The Korean Society of Mechanical Engineers 2022 Fall Conference, November 2022.
  9. BongSeok Kim, Shinseon Kang, and Kyunghoon Lee, “Component library-based reconstruction of an aircraft wing structure and a rapid yet accurate structural analysis,” The Korean Society for Aeronautical and Space Sciences 2022 Fall Conference, November 2022.
  10. BongSeok Kim, Shinseon Kang, and Kyunghoon Lee, “Rapid yet accurate elastostatic analysis of a large-scale structure: application to an aircraft wing structure,” The Korean Institute of Military Science and Technology 2022 Spring Conference, June 2022.
  11. BongSeok Kim, Shinseon Kang, and Kyunghoon Lee, “Rapid yet accurate elastostatic analysis and geometric parameterization of an aircraft wing structure,” The Korean Society of Mechanical Engineers 2022 Spring Conference, May 2022.
  12. BongSeok Kim, Shinseon Kang, and Kyunghoon Lee, “A static condensation reduced basis element method applied to the linear elastostatic analysis of an aircraft wing structure,” Computational Structural Engineering Institute of Korea 2022 Spring Conference, April 2022.
  13. BongSeok Kim, Shinseon Kang, and Kyunghoon Lee, “Rapid yet accurate linear elastostatic analysis of an aircraft wing structure,” The Korean Society for Aeronautical and Space Sciences 2022 Spring Conference, April 2022.

Reviewer

  • Mathematics and Computers in Simulation, 2026

News

July 2026 Our paper “A Hyperbolic Neural Closure for M1 Radiation Transfer” is currently under review at Computer Methods in Applied Mechanics and Engineering (CMAME) .
July 2026 Our paper “pADAM: A Plug-and-Play All-in-One Diffusion Architecture for Multi-Physics Learning” is under revision at Nature npj Artificial Intelligence.
July 2026 Presented our work on hyperbolic neural closure at the SIAM Annual Meeting 2026, in the minisymposium “Stochastic and Data-Driven Modeling: Computational Methods for Moment Models in Radiative Transfer and Kinetic Theory” , Cleveland, Ohio, USA.
June 2026 Our molecular dynamics paper was accepted for publication in The Journal of Physical Chemistry B.
May 2026 Our paper on Weak-Form Evolutionary Kolmogorov–Arnold Networks was accepted by Computer Methods in Applied Mechanics and Engineering (CMAME) .
November 2025 Presented our work on molecular dynamics simulations of transcritical mass transfer at the International Conference of Fluid Dynamics 2025 (ICFD 2025), Sendai, Japan.