Hi, 👋 I’m Jinsu Kim. I’m a Ph.D. graduate student in Department of Mechanical and Aerospace Engineering at Princeton University. I received my B.S.and M.S. in Nuclear Engineering and Physics at Seoul National University. My research lies at plasma physics, physics-informed scientific computing, data-driven modeling and control.

I am very interested in the potential of machine learning application in industry. As an applied scientist intern at GaussLabs, I researched data-driven modeling for plasma etching and diffusion process. I am now open to any internship for computational simulation and data-driven modeling.

My research goal is to bridge the gap between physics and machine learning, expecially in data-driven modeling and control for nonlinear dynamics. Now, I am working on two topics: Structure-preserving model order reduction for Vlasov-Possion Plasma systems, and Data assimilation for Magnetohydrodynamic systems. If you have an interest in coworking, please feel free to contact me!

My computational works are shared on the github link. Please see and share your opinions.

Research area

Dynamics and Control

📌 Model reduction on nonlinear dynamic systems

• Structure-preserving model reduction for Vlasov-Poisson plasma system
  • Development of Particle-In-Cell simulation in electrostatic plasma system
  • Symplectic model reduction for preserving Hamiltonian form in reduced space

📌 Computational plasma physics

• Symplectic integration of Particle-In-Cell method for plasma kinetic simulations
  • Development on PIC with symplectic integration in electromagnetic plasma system
  • Spectral solver for electromagnetic PIC and application of parallel computation

Nuclear Fusion

📌 Data-driven modeling for fusion plasma and optimized control

• Disruption prediction in KSTAR tokamak plasma with Deep Learning
  • Development of multi-modal deep neural network with multiple signals and IVIS for predicting thermal quench
  • Uncertainty modeling for high precision and causality estimation in disruption prediction with Bayesian deep learning
• Data-driven modeling and control for tokamak plasma operation
  • Development of physics-informed neural network for tokamak plasma simulation (Grad-Shafranov Physics-Informed Neural Network: GS-PINN)
  • Investigation for multi-objective plasma control with reinforcement learning

📌 Reactor Design Optimization

• Design optimization of a tokamak reactor with data-driven approaches
  • Development of tokamak reactor design computation code
  • Reactor design optimization for high performance and sustainable plasma operation based on bayesian optimization and reinforcement learning

AI in Nuclear Fusion: Bridging the gap between science and engineering