Tomoki Koike

About Me

I am a Ph.D. student in the School of Aerospace Engineeing at Georgia Institute of Technology, where I am also pursuing a Master’s degree in the School of Computational Science and Engineering. I am a member of the Aerospace Computational Engineering (ACE) lab supervised under the helm of Dr. Elizabeth Qian.

My research focuses on developing data-driven model reduction techniques for large-scale dynamical systems, with applications in areas such as fluid dynamics and wind turbine modeling. Leveraging my background in control theory, I also work on creating stability-informed reduced-order models to ensure both efficiency and reliability. My ultimate goal is to develop innovative methods that enable faster, more cost-effective, and physically accurate simulations of large-scale systems, paving the way for advancements in digital twins, sim-to-real transitions, and other computational methods for real-world science and engineering challenges.

Research Interests

Model Reduction: proper orthogonal decomposition (POD), operator inference (OpInf)
Control Theory: system stability analysis, controller synthesis, stable surrogate modeling
Randomized Numerical Linear Algebra: randomized singular value decomposition (rSVD), randomized least-squares
Scientific Machine Learning: physics-informed machine learning, neural operators
Others: multifidelity modeling, inverse problems

Upcoming Talks and Activities

[Mar. 2025] I will be presenting my work on a streaming model reduction approach and data-driven Lyapunov function inference approach at SIAM CSE in Fort Worth, TX.