26 citations · 66 across the 35 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…
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…
A Domain-Decomposition Model Reduction Method for Linear Convection-Diffusion Equations with Random Coefficients
Lin Mu, Guannan Zhang
We develop a domain-decomposition model reduction method for linear steady-state convection-diffusion equations with random coefficients. Of particular interest to this effort are…
An improved discrete least-squares/reduced-basis method for parameterized elliptic PDEs
Max Gunzburger, Michael Schneier, Clayton Webster +1
It is shown that the computational efficiency of the discrete least-squares (DLS) approximation of solutions of stochastic elliptic PDEs is improved by incorporating a reduced-basi…
On the Lebesgue Constant of Weighted Leja Points for Lagrange Interpolation on Unbounded Domains
Peter Jantsch, Clayton G. Webster, Guannan Zhang
This work focuses on weighted Lagrange interpolation on an unbounded domain, and analyzes the Lebesgue constant for a sequence of weighted Leja points. The standard Leja points are…
A sparse grid method for Bayesian uncertainty quantification with application to large eddy simulation turbulence models
Hoang A. Tran, Clayton G. Webster, Guannan Zhang
There is wide agreement that the accuracy of turbulence models suffer from their sensitivity with respect to physical input data, the uncertainties of user-elected parameters, as w…
An Efficient Meshfreee Implicit Filter for Nonlinear Filtering Problems
Feng Bao, Yanzhao Cao, Clayton Webster +1
In this paper, we propose a meshfree approximation method for the implicit filter developed in [2], which is a novel numerical algorithm for nonlinear filtering problems. The impli…
Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients
Nick Dexter, Clayton Webster, Guannan Zhang
This work analyzes the overall computational complexity of the stochastic Galerkin finite element method (SGFEM) for approximating the solution of parameterized elliptic partial di…
Accelerating stochastic collocation methods for partial differential equations with random input data
Diego Galindo, Peter Jantsch, Clayton G. Webster +1
This work proposes and analyzes a generalized acceleration technique for decreasing the computational complexity of using stochastic collocation (SC) methods to solve partial diffe…
Numerical Methods for a Class of Nonlocal Diffusion Problems with the Use of Backward SDEs
Guannan Zhang, Weidong Zhao, Clayton Webster +1
We propose a novel numerical approach for nonlocal diffusion equations [8] with integrable kernels, based on the relationship between the backward Kolmogorov equation and backward…
Split-step Milstein methods for multi-channel stiff stochastic differential systems
V. Reshniak, A. Q. M. Khaliq, D. A. Voss +1
We consider split-step Milstein methods for the solution of stiff stochastic differential equations with an emphasis on systems driven by multi-channel noise. We show their strong…