Control

Hybrid Dynamical System Modeling and Inference via Machine Learning
A unified ML framework—Event-Automata + Physics-Embedded Neural ODEs + Neural Substitute Solver—for accurate, real-time inference of hybrid (continuous–discrete) dynamics with reliable Sim-to-Real transfer to edge hardware.
Hybrid Dynamical System Modeling and Inference via Machine Learning
Design and Control of High-Frequency DC–DC Converters
From multi-DOF modulation and universal phase-shift control to device-aware electro-thermal modeling and CDT-MPC, this project delivers efficient, soft-switching, and control-ready designs for DAB/MMAB at 50–400 kHz+.
Design and Control of High-Frequency DC–DC Converters
Edge Digital Twins for Power Electronics
Sim-to-Real Edge Digital Twins that fuse event-aware physics with neural operators for sub-microsecond inference, online parameter self-calibration, and control integration on FPGA/MPSoC.
Edge Digital Twins for Power Electronics