12 papers
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…
Cross-Domain Generalization of Multimodal LLMs for Global Photovoltaic Assessment
Muhao Guo, Yang Weng
The rapid expansion of distributed photovoltaic (PV) systems poses challenges for power grid management, as many installations remain undocumented. While satellite imagery provides…
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…
Latent Mixture of Symmetries for Sample-Efficient Dynamic Learning
Haoran Li, Chenhan Xiao, Muhao Guo +1
Learning dynamics is essential for model-based control and Reinforcement Learning in engineering systems, such as robotics and power systems. However, limited system measurements,…