6 papers
MoRF-AST: Calibrated Probabilistic Virtual Sensing for Structural Monitoring under Changing Operating Conditions
Wingho Feng, Quanwang Li, Ming Zhong +2
Probabilistic full-field reconstruction provides uncertainty-aware response evidence for structural reliability assessment, yet inference from sparse and noisy measurements remains…
Event-Structured Physics-Informed Neural Networks for Differentiable Critical Clearing Boundaries
Baoli Hao, Chenxi Hu, Ming Zhong +1
Transient-stability assessment determines whether a power system can recover after a disturbance and is therefore essential to preventing generator trips and cascading outages. A k…
Learning Stochastic Dynamical Systems with Structured Noise
Ziheng Guo, James Greene, Ming Zhong
Stochastic differential equations (SDEs) are a ubiquitous modeling framework that finds applications in physics, biology, engineering, social science, and finance. Due to the avail…
Noise-Aware System Identification for High-Dimensional Stochastic Dynamics
Ziheng Guo, Igor Cialenco, Ming Zhong
Stochastic dynamical systems are ubiquitous in physics, biology, and engineering, where both deterministic drifts and random fluctuations govern system behavior. Learning these dyn…
Learning Stochastic Dynamics from Data
Ziheng Guo, Igor Cialenco, Ming Zhong
We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations. Our m…
PINNs-Based Uncertainty Quantification for Transient Stability Analysis
Ren Wang, Ming Zhong, Kaidi Xu +2
This paper addresses the challenge of transient stability in power systems with missing parameters and uncertainty propagation in swing equations. We introduce a novel application…