Sim

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
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
Event-Driven Efficient Simulation of Hybrid Dynamical Systems
Event-driven HIL simulation that replaces tiny fixed steps with switching-aware sampling and variable-order solvers (SCED/DHT, VTR-CHIL, DAT/SEO), enabling high-frequency, large-scale power electronics on commodity CPUs/MPSoCs.
Event-Driven Efficient Simulation of Hybrid Dynamical Systems
Large-Scale Cyber-Physical System Co-Simulation
Event-axis, synchronization-aware co-simulation that scales CHIL/PCCO from kW MMCs to MW-level converters by key-frame prediction, event-driven data rematching, and hybrid CPU–FPGA execution—boosting fidelity and easing real-time constraints.
Large-Scale Cyber-Physical System Co-Simulation
Sub-Microsecond Real-Time FPGA Numerical Solver
Deterministic sub-µs FPGA solvers (12.5–75 ns) combining semi-implicit leapfrog, topology-aware partitioning, and IMEX techniques for stability, low memory, and controller-accurate HIL.
Sub-Microsecond Real-Time FPGA Numerical Solver