Tianzhi Li 李天志
 |
Ph.D. Student |
About me
I am a final-year PhD student at Peking University (PKU), working with Professors Jinzhi Wang and Zhisheng Duan. From Dec 2024 to Nov 2025, I was fortunate to work as a visiting PhD at Nanyang Technological University (NTU), Singapore, with Professor François Gay-Balmaz. Prior to that, I received my B.Sc. in Mathematics from Beijing Institute of Technology (BIT) in 2021 under the supervision of Professor Donghua Shi.
Research Interest
My research interest lies at geometric control and learning of robotic and mechanical systems, which includes the following topics:
Geometric Mechanics & Control (especially stochastic & nonholonomic parts)
Physics-Informed Learning (for robotic and mechanical systems)
Structure-Preserving Discretization (variational integrators for ODEs/PDEs)
Stochastic Mechanics and Estimation (for robot systems & flexible systems)
Selected Research
 |
Variational Unscented Kalman Filter on Matrix Lie Groups Automatica, 172: 111995, 2025 (Regular Paper). We propose a family of computationally efficient unscented Kalman filters (UKF-Vs) for mechanical systems on matrix Lie groups. |
 |
Reduced Dynamics and Geometric Optimal Control of Nonequilibrium Thermodynamics: Gaussian Case Automatica, 164: 111626, 2024 (Regular Paper). We study the geometric structures of n-DOF Gaussian distributions, and we propose a geometric optimal control algorithm for minimum-energy optimal control problem of Gaussian distributions. |
 |
Physics-Informed Gaussian Process Learning on Lie Groups Journal of Guidance, Control, and Dynamics, 48 (11), pp. 2654-2662, 2025. We introduce a physics-informed Gaussian process learning method for mechanical systems on Lie groups and on a special class of homogeneous manifolds based on discrete mechanics theory. |
News
Aug 2025: Our paper Structure-Preserving Unscented Kalman Filter for Planar Mobile Robots has been accepted to IEEE Control Systems Letters (IEEE L-CSS).
Aug 2025: Our paper Physics-Informed Gaussian Process Learning on Lie Groups has been accepted to Journal of Guidance, Control, and Dynamics (JGCD).
Jul 2025: Our two papers Variational Principle for Stochastic Nonholonomic Systems Part I & Part II (joint work with Professors François Gay-Balmaz, Donghua Shi, and Jinzhi Wang) have been accepted by the International Conference on Geometric Science of Information (GSI 2025).
Sep 2024: Our paper Variational Unscented Kalman Filter on Matrix Lie Groups has been accepted to Automatica.
Feb 2024: Our paper Reduced Dynamics and Geometric Optimal Control of Nonequilibrium Thermodynamics: Gaussian Case has been accepted by Automatica.