36 citations · 99 across the 7 of their papers we have counts for
7 papers
Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate
Lianghao Cao, Keyi Wu, J. Tinsley Oden +2
Identifying parameters of computational models from experimental data, or model calibration, is fundamental for assessing and improving the predictability and reliability of comput…
Large-scale Bayesian optimal experimental design with derivative-informed projected neural network
Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen +1
We address the solution of large-scale Bayesian optimal experimental design (OED) problems governed by partial differential equations (PDEs) with infinite-dimensional parameter fie…
An efficient method for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placemen
Keyi Wu, Peng Chen, Omar Ghattas
Optimal experimental design (OED) plays an important role in the problem of identifying uncertainty with limited experimental data. In many applications, we seek to minimize the un…
Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities
Peng Chen, Keyi Wu, Omar Ghattas
We propose a high dimensional Bayesian inference framework for learning heterogeneous dynamics of a COVID-19 model, with a specific application to the dynamics and severity of COVI…
A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design
Keyi Wu, Peng Chen, Omar Ghattas
We develop a fast and scalable computational framework to solve large-scale and high-dimensional Bayesian optimal experimental design problems. In particular, we consider the probl…
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions
Peng Chen, Keyi Wu, Joshua Chen +2
We propose a fast and scalable variational method for Bayesian inference in high-dimensional parameter space, which we call projected Stein variational Newton (pSVN) method. We exp…
A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification
Keyi Wu, Jinglai Li
In this work we consider a class of uncertainty quantification problems where the system performance or reliability is characterized by a scalar parameter . The performance para…