10 papers
IEnSF: Iterative Ensemble Score Filter for Reducing Error in Posterior Score Estimation in Nonlinear Data Assimilation
Zezhong Zhang, Feng Bao, Guannan Zhang
The Ensemble Score Filter (EnSF) is a score-based diffusion model approach for solving high-dimensional and nonlinear data assimilation problems. While initial applications of EnSF…
A Two-Step Ensemble Score Filter for Data Assimilation in Partially Observed Systems
Zixiang Xiong, Feng Bao, Hristo G. Chipilski +3
Data assimilation blends model forecasts with observations to estimate the evolving state of complex dynamical systems, but sparse observing networks remain challenging because uno…
Diffusion Model-Based Data Assimilation for Real-World Energy Consumption Forecasting
Ruoyu Hu, Dahai Yu, Feng Bao +2
Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management. In practice, however, complete and timely…
Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Prediction
Xiao Wang, Zezhong Zhang, Isaac Lyngaas +10
Accurate weather and climate prediction relies on data assimilation (DA), which estimates the Earth system state by integrating observations with models. While exascale computing h…
Error estimates of a training-free diffusion model for high-dimensional sampling
Pengjun Wang, Zezhong Zhang, Minglei Yang +3
Score-based diffusion models are a powerful class of generative models, but their practical use often depends on training neural networks to approximate the score function. Trainin…
An efficient probabilistic scheme for the exit time probability of -stable Lévy process
Minglei Yang, Diego del-Castillo-Negrete, Guannan Zhang
The α-stable Lévy process, commonly used to describe Lévy flight, is characterized by discontinuous jumps and is widely used to model anomalous transport phenomena. In this stud…