I'm a Ph.D graduate student in the Department of Mechanical and Aerospace Engineering at Princeton University. My research interests are in data-driven modeling and control for nonlinear dynamics. My previous research aimed at plasma heat transport and physics-informed data-driven modeling for zeroth order plasma dynamics and control. Now, I am working on theoretical aspects, including modeling dynamic systems, control theory, and structure-preserving model reduction with machine learning. I shared several computational works on Github. Feel free to contact me if you are interested in my research, work, or anything else you want to know about me.
- CV: [Jinsu Kim, CV]
- Linkedin:[Linkedin : zinzinbin]
- Youtube: [AI in Nuclear Fusion: Bridging the gap between science and engineering, 8th Pseudocon]
- Symplectic model reduction on nonlinear systems
- Physics-informed machine learning and modeling dynamics
- Control theory
- Tokamak plasma disruption prediction based on data-driven modeling
- Data-driven tokamak plasma dynamics modeling and control with deep reinforcement learning
- Design optimization of a tokamak fusion reactor based on data-driven optimization
- ML application on plasma etching process in Virtual Metrology
- Physics-based plasma etching process control
- WelfareForEveryone : https://play.google.com/store/apps/details?id=com.product.welfareapp&pli=1
- github link : https://github.com/21WelfareForEveryone/WelfareForEveryOne
