458 citations · 1.4k across the 32 of their papers we have counts for
4 papers · 1 filter
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
Shijie Zhong, Wanggang Shen, Tommie Catanach +1
Optimal experimental design (OED) provides a systematic approach to quantify and maximize the value of experimental data. Under a Bayesian approach, conventional OED maximizes the…
Embedded Model Error Representation for Bayesian Model Calibration
Khachik Sargsyan, Xun Huan, Habib N. Najm
Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, al…
Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions
Xun Huan, Cosmin Safta, Khachik Sargsyan +4
Compressive sensing is a powerful technique for recovering sparse solutions of underdetermined linear systems, which is often encountered in uncertainty quantification analysis of…
Gradient-based stochastic optimization methods in Bayesian experimental design
Xun Huan, Youssef M. Marzouk
Optimal experimental design (OED) seeks experiments expected to yield the most useful data for some purpose. In practical circumstances where experiments are time-consuming or reso…