13 papers · 1 filter
Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution
Chenhan Xiao, Xinyu He, Haoran Li +2
Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications. Recent synthetic grid generation…
Graph Structure Learning with Privacy Guarantees for Open Graph Data
Muhao Guo, Jiaqi Wu, Yizheng Liao +3
Publishing open graph data while preserving individual privacy remains challenging when data publishers and data users are distinct entities. Although differential privacy (DP) pro…
LASS-ODE: Scaling ODE Computations to Connect Foundation Models with Dynamical Physical Systems
Haoran Li, Chenhan Xiao, Lihao Mai +2
Foundation models have transformed language, vision, and time series data analysis, yet progress on dynamic predictions for physical systems remains limited. Given the complexity o…
MOE-GL: A Family of Probabilistic Load Forecasters That Scales to Massive Customers
Haoran Li, Zhe Cheng, Muhao Guo +4
Probabilistic load forecasting is widely studied and underpins power system planning, operation, and risk-aware decision making. Deep learning forecasters have shown strong ability…
Efficient Manifold-Constrained Neural ODE for High-Dimensional Datasets
Muhao Guo, Haoran Li, Yang Weng
Neural ordinary differential equations (NODE) have garnered significant attention for their design of continuous-depth neural networks and the ability to learn data/feature dynamic…
Modeling Time Series Dynamics with Fourier Ordinary Differential Equations
Muhao Guo, Yang Weng
Neural ODEs (NODEs) have emerged as powerful tools for modeling time series data, offering the flexibility to adapt to varying input scales and capture complex dynamics. However, t…