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Research
Research
The modeling and control of modern applications such as autonomous driving, soft robotics or human-robot interaction can be excessively time-consuming or even unfeasible due to the growing complexity of technical systems. To address these challenges, data-driven models are a viable solution that requires minimal expert knowledge and have shown remarkable results. However, a major drawback of these models is their unpredictable outcomes, which limits their applicability to non-safety critical systems.
Our research focuses on safe learning-based modeling and control of physical systems. We are working on developing novel data-driven control methods that ensure both safe operation and high-performance for the closed-loop system. In addition, we are exploring algorithms that incorporate physical prior knowledge into learning-based models to enhance their generalizability, reliability and interpretability. The research outcomes contribute to the development of safe, robust, and intelligent control methods for physical systems.
News
News
05/2025 I will present our recent work on Physics-informed system identification at the Workshop on Nonlinear System Identification Benchmarks
04/2025 Check out our new tutorial paper on "Safe Physics-Informed Machine Learning for Dynamics and Control" (will be presented at ACC 2025)
04/2025 Congratulations to Peilun Li and Kaiyuan Tan on the acceptance of their paper "NAPI-MPC: Neural Accelerated Physics-Informed MPC for Nonlinear PDE Systems" at L4DC 2025.
01/2025 Congratulations to Kaiyuan Tan and Peilun Li on the acceptance of their paper "PnP-PIML: Physics-informed Learning of Outlier Dynamics using Uncertainty Quantified Port-Hamiltonian Models" at ICRA 2025.
12/2024 Congratulations to Peilun Li who was selected for Honorable Mention for the 2024-2025 CRA Outstanding Undergraduate Researcher Award (URA).
12/2024 I will present at the CDC 2024 workshop Learning Dynamics from Data: Fusing Machine Learning and System Identification
10/2024 Tobias Wolff from University of Hannover is visiting our group. Welcome!
08/2024 Our paper Interpretable Finite State Machine Controller: A Case Study on Lane Merge Yield Mode has been accepted at ITSC 2024.
08/2024 I presented at the Port-Hamiltonian Systems Seminar series at the University of Wuppertal, Germany.
07/2024 Our group has three presentations at the 16th World Congress on Computational Mechanics in Vancouver.
07/2024 We're welcoming our new team members Mohammad Ali and Kendra Givens!
07/2024 We presented our work on Physics-informed learning of PDEs with uncertainty quantification at the L4DC in Oxford.
07/2024 We organized a workshop on physics-informed learning with fantastic talks and a great tutorial session at the American Control Conference (ACC).
06/2024 Keeping strong ties with the European control community by presenting our recent work at the ECC Workshop on Physics-Informed Learning in Control!
06/2024 We presented our new Python toolbox for Bayesian learning of Port-Hamiltonian systems at the 8th IFAC Workshop on Lagrangian and Hamiltonian Methods for Non Linear Control
05/2024 I gave a presentation at the Artificial Intelligence for Robust Engineering & Science Workshop (AIRES), PNNL
04/2024 I gave a virtual presentation at the UNC Charlotte Robotics and Controls Seminar series
04/2024 Peilun Li and Kaiyuan Tan got a paper accepted at the 8th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control.
02/2024 Peilun Li and Kaiyuan Tan will present their most recent works on physics-informed learning at the 16th World Congress on Computational Mechanics
01/2024 We will organize a workshop on Physics-informed Machine Learning for Modeling, Control, and Optimization at the American Control Conference, July 10-12, 2024.
12/2023 Our workshop on "Physics-informed Learning for Control and Optimization" at the CDC 2023 was a great success with more than 80 registered participants, making it one of the largest workshops.
11/2023 I will present our most recent work on Gaussian Process Port-Hamilonian Systems at the Second Workshop on Physics Enhancing Machine Learning in Applied Mechanics [slides]
08/2023 Vanderbilt University is supporting my work on data-driven control for soft robots with a RAMP award
08/2023 Peilun presented a poster at the 25th Anniversary of the Institut of Software Integrated Systems
07/2023 I gave a seminar on "Physics-enhanced Gaussian Processes for Learning of Electromechanical Systems" at the DDPS webinar series. Check out the recording.
06/2023 I will present our poster on "Physics-enhanced Gaussian Process Variational Autoencoder" at L4DC in Philadelphia
05/2023 The Chambers Family Summer Research Fund will support Peilun's summer research in the School of Engineering
04/2023 If you are interested in Gaussian Process based control, please join our workshop at IFAC WC
03/2023 I will present our most recent work on Gaussian Process Port-Hamilonian Systems at the Artificial Intelligence for Robust Engineering and Science (AIRES) workshop
03/2023 Our paper "Physics-enhanced Gaussian Process Variational Autoencoder" has been accepted at L4DC 2023.
03/2023 Two accepted paper at IFAC WC. In addition, we will organize a workshop on Gaussian process based identification and Control
03/2023 I gave a seminar on Gaussian Process Port-Hamiltonian systems at University of Wuppertal
01/2023 I offer a project at the Vanderbilt School of Engineering Summer Research Program for undergraduates.
01/2023 I've started my new position as Assistant Professor of Computer Science at Vanderbilt University.
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