Sungwon Kim

I am a Ph.D. candidate in the Graduate School of Data Science (GSDS) at KAIST, where I am advised by Prof. Chanyoung Park. I hold a B.S. degree in Civil, Environmental and Architectural Engineering from Korea University.

I’m actively on research with my best colleagues at Data Science and Artificial Intelligence Lab.

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🔬 Core Research Focus

AI Surrogate Modeling for CAE and PDE solvers (Neural Operators)

My primary research is dedicated to developing high-fidelity AI surrogate models to replace computationally intensive 3D CAE simulations (e.g., structural mechanics, fluid dynamics, injection molding analysis, etc.) and PDE solvers in engineering (i.e., neural operators). This work directly accelerates engineering design cycles and lowers computational costs.

Keywords: Physics AI (Engineering), 3D Simulation, Physics-Informed Neural Networks (PINNs), Neural Operators

Key Focus:

• Designing effective 3D representations (point-cloud, mesh, etc.) to achieve maximal speed and accuracy in surrogate models.
• Integrating LLMs to significantly enhance model usability and streamline complex engineering workflows for practical application.

Projects:
Physics-AI, Learning-based 3D Simulation (Collaboration with LG Electronics)

• How can we create a learning-based alternative to the FEM for 3D inputs with highly complex geometries, given the initial and boundary conditions? (Point-cloud based)
• How can we efficiently interact with opposing surfaces while maintaining computational efficiency? (Mesh based)