26 citations · 66 across the 35 of their papers we have counts for
38 papers
A Pre-trained Variational Autoencoder for Gyrokinetic Plasma Turbulence Surrogate Modeling
Minglei Yang, Marshall Nicholson, Diego Del-Castillo-Negrete +2
Machine learning surrogate models offer a promising path toward accelerating plasma turbulence simulations. We present PreVAE-Turb, a surrogate modeling framework that leverages pr…
Score-Based Generative Data Assimilation for Integrating Aggregated Surveillance Data into Agent-Based Models in Epidemic Tracking
Siming Liang, Jacob Hauck, Minglei Yang +3
Reliable epidemic monitoring often requires inferring regional infection burden and transmission heterogeneity from noisy, spatially aggregated, and potentially sparse surveillance…
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…
Deciphering Majorana Zero Modes in Topological Superconductor FeTe0.55Se0.45 with Machine-Learning-Assisted Spectral Deconvolution
Jewook Park, Hoyeon Jeon, Dongwon Shin +4
Unambiguous identification of Majorana zero modes (MZMs) in topological superconductors (TSCs) remains a challenge due to complex in-gap states that can also produce zero-bias cond…
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 study,…
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…
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…
Bifidelity Parameter Estimation Using Conditional Diffusion Models
Caroline Tatsuoka, Minglei Yang, Dongbin Xiu +1
We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distr…
Joint State-Parameter Estimation for the Reduced Fracture Model via the United Filter
Toan Huynh, Thi-Thao-Phuong Hoang, Guannan Zhang +1
In this paper, we introduce an effective United Filter method for jointly estimating the solution state and physical parameters in flow and transport problems within fractured poro…
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…
Nonuniform random feature models using derivative information
Konstantin Pieper, Zezhong Zhang, Guannan Zhang
We propose nonuniform data-driven parameter distributions for neural network initialization based on derivative data of the function to be approximated. These parameter distributio…
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…
Hierarchical Bayesian approach for adaptive integration of Bragg peaks in time-of-flight neutron scattering data
Viktor Reshniak, Xiaoping Wang, Guannan Zhang +2
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) operates in the event mode. Time-of-flight (TOF) information about each detected neutron is collected se…
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…
Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
Minglei Yang, Pengjun Wang, Ming Fan +3
We introduce a conditional pseudo-reversible normalizing flow for constructing surrogate models of a physical model polluted by additive noise to efficiently quantify forward and i…
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…
A pseudo-reversible normalizing flow for stochastic dynamical systems with various initial distributions
Minglei Yang, Pengjun Wang, Diego del-Castillo-Negrete +2
We present a pseudo-reversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with different initial distributi…