68 citations · 68 across the 2 of their papers we have counts for
6 papers
Optimal Stopping for Sequential Bayesian Experimental Design
Chen Cheng, Xun Huan
Sequential Bayesian experimental design is often formulated as a fixed-horizon policy optimization problem, in which the number of experiments is specified before data collection b…
Optimal experimental design: Formulations and computations
Xun Huan, Jayanth Jagalur, Youssef Marzouk
Questions of `how best to acquire data' are essential to modeling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental desi…
A Multi-fidelity Estimator of the Expected Information Gain for Bayesian Optimal Experimental Design
Thomas E. Coons, Xun Huan
Optimal experimental design (OED) is a framework that leverages a mathematical model of the experiment to identify optimal conditions for conducting the experiment. Under a Bayesia…
Bayesian Covariance Uncertainty for Adaptive Pilot-Sampling Termination in Multi-fidelity Uncertainty Quantification
Thomas E. Coons, Aniket Jivani, Xun Huan
Monte Carlo integration becomes prohibitively expensive when each sample requires a high-fidelity model evaluation. Multi-fidelity uncertainty quantification methods mitigate this…
A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Design
Atlanta Chakraborty, Xun Huan, Tommie Catanach
Conventional Bayesian optimal experimental design seeks to maximize the expected information gain (EIG) on model parameters. However, the end goal of the experiment often is not to…
Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems
Zhiyi Chen, Harshal Maske, Devesh Upadhyay +3
This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs). A new feedforward control scheme is developed…