Publications (11)
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…
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…
Automated spacing measurement of formwork system members with 3D point cloud data
Keyi Wu, Samuel A. Prieto, Eyob Mengiste +1
The formwork system belonging to the temporary structure plays an important role in the smooth progress and successful completion of a construction project. Ensuring that the formw…
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…
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…
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…