39 citations · 46 across the 4 of their papers we have counts for
4 papers
Mean--Variance Risk-Aware Bayesian Optimal Experimental Design for Nonlinear Models
Wanggang Shen, Xun Huan
We propose a variance-penalized formulation of Bayesian optimal experimental design for nonlinear models that augments the classical expected utility criterion with a penalty on ut…
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…
Variational Sequential Optimal Experimental Design using Reinforcement Learning
Wanggang Shen, Jiayuan Dong, Xun Huan
We present variational sequential optimal experimental design (vsOED), a novel method for optimally designing a finite sequence of experiments within a Bayesian framework with info…
Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
Wanggang Shen, Xun Huan
We present a mathematical framework and computational methods to optimally design a finite number of sequential experiments. We formulate this sequential optimal experimental desig…