2 citations · 9 across the 17 of their papers we have counts for
18 papers
From Variational Optimization to Flow-Based Transport: Posterior-Geometry Regularization for Ill-Conditioned Data Assimilation
Siming Liang, Feng Bao, Hristo G. Chipilski +2
Variational data assimilation computes a Bayesian update through optimization on a fixed posterior geometry, whose conditioning is determined by the prior covariance and the observ…
Data-Assimilation-Assisted Reinforcement Learning for Power Grid Control under Load Uncertainty
Ruoyu Hu, Feng Bao, Zezhong Zhang +2
Reliable power grid control requires sequential decisions under transmission constraints, time-varying demand, renewable variability, and imperfect load information. We investigate…
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 Earth system prediction requires state inference from incomplete observations, but conventional two-stage data assimilation (DA) is computationally prohibitive because rep…
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 Score-based Diffusion Model Approach for Adaptive Learning of Stochastic Partial Differential Equation Solutions
Toan Huynh, Ruth Lopez Fajardo, Guannan Zhang +2
We propose a novel framework for adaptively learning the time-evolving solutions of stochastic partial differential equations (SPDEs) using score-based diffusion models within a re…
On the sensitivity of different ensemble filters to the type of assimilated observation networks
Zixiang Xiong, Siming Liang, Feng Bao +2
Recent advances in data assimilation (DA) have focused on developing more flexible approaches that can better accommodate nonlinearities in models and observations. However, it rem…
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions
Hoang Tran, Zezhong Zhang, Feng Bao +2
We propose a hybrid generative model for efficient sampling of high-dimensional, multimodal probability distributions for Bayesian inference. Traditional Monte Carlo methods, such…
Ensemble score filter with image inpainting for data assimilation in tracking surface quasi-geostrophic dynamics with partial observations
Siming Liang, Hoang Tran, Feng Bao +3
Data assimilation plays a pivotal role in understanding and predicting turbulent systems within geoscience and weather forecasting, where data assimilation is used to address three…
A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics
Junqi Yin, Siming Liang, Siyan Liu +4
The weather and climate domains are undergoing a significant transformation thanks to advances in AI-based foundation models such as FourCastNet, GraphCast, ClimaX and Pangu-Weathe…
Nonlinear ensemble filtering with diffusion models: Application to the surface quasi-geostrophic dynamics
Feng Bao, Hristo G. Chipilski, Siming Liang +2
The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here we explore another promi…
An Online Algorithm for Solving Feedback Optimal Control Problems with Partial Observations
Siming Liang, Ruoyu Hu, Feng Bao +2
This paper presents a novel methodology to tackle feedback optimal control problems in scenarios where the exact state of the controlled process is unknown. It integrates data assi…
Improving the Expressive Power of Deep Neural Networks through Integral Activation Transform
Zezhong Zhang, Feng Bao, Guannan Zhang
The impressive expressive power of deep neural networks (DNNs) underlies their widespread applicability. However, while the theoretical capacity of deep architectures is high, the…
A Unified Filter Method for Jointly Estimating State and Parameters of Stochastic Dynamical Systems via the Ensemble Score Filter
Feng Bao, Guannan Zhang, Zezhong Zhang
This paper tackles the intricate task of jointly estimating state and parameters in data assimilation for stochastic dynamical systems that are affected by noise and observed only…
Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation
Yanfang Liu, Minglei Yang, Zezhong Zhang +3
We present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks, normalizing flows, va…
An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems
Feng Bao, Zezhong Zhang, Guannan Zhang
We propose an ensemble score filter (EnSF) for solving high-dimensional nonlinear filtering problems with superior accuracy. A major drawback of existing filtering methods, e.g., p…
TransNet: Transferable Neural Networks for Partial Differential Equations
Zezhong Zhang, Feng Bao, Lili Ju +1
Transfer learning for partial differential equations (PDEs) is to develop a pre-trained neural network that can be used to solve a wide class of PDEs. Existing transfer learning ap…