5 papers
FieldSeer I: Physics-Guided World Models for Long-Horizon Electromagnetic Dynamics under Partial Observability
Ziheng Guo, Fang Wu, Maoxiong Zhao +2
We introduce FieldSeer I, a geometry-aware world model that forecasts electromagnetic field dynamics from partial observations in 2-D TE waveguides. The model assimilates a short p…
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…
MgFNO: Multi-grid Architecture Fourier Neural Operator for Parametric Partial Differential Equations
Zi-Hao Guo, Hou-Biao Li
Neural operators are a new type of models that can map between function spaces, allowing trained models to emulate the solution operators of partial differential equations (PDEs).…
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…