critical clearing time 1event-structured modeling 1physics-informed neural networks 1power system dynamics 1transient stability assessment 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.LG2026
Event-Structured Physics-Informed Neural Networks for Differentiable Critical Clearing Boundaries
Baoli Hao, Chenxi Hu, Ming Zhong +1
The paper introduces an event-structured physics‑informed neural network (ES‑PINN) that models pre‑fault, fault‑on, and post‑clearing dynamics to accurately estimate the critical c…
math.NA2026
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…
stat.ML2025
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…