Peilun Li
Papers
1
Total Citations
1
H-Index
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About
Peilun Li is a researcher at the forefront of integrating physics-informed machine learning with robotics and autonomous systems. Their key research areas include uncertainty quantification, model-based control, and the development of hybrid data-driven and physics-constrained models for dynamic environments. Li’s major contribution lies in pioneering plug-and-play frameworks that combine port-Hamiltonian system theory with deep learning, enabling robots to predict agent trajectories and obstacle movements with both high accuracy and calibrated uncertainty—a critical advance for safe autonomy. Their most cited work, "Plug-and-Play Physics-Informed Learning Using Uncertainty Quantified Port-Hamiltonian Models" (2025), has already garnered early citations, signaling its growing influence in bridging theoretical physics and practical robotics. By addressing the challenge of unknown dynamics in real-world scenarios, Li’s research directly impacts applications in autonomous driving, drone navigation, and human-robot interaction. Their work stands out for its elegant fusion of rigorous mathematical structure with data-driven flexibility, offering a principled path toward reliable, interpretable AI in safety-critical systems.
Research Focus
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Top Papers
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