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20222025
most citedDesign of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter

2 citations · 8 across the 9 of their papers we have counts for

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stat.CO2024★ 1 cited

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…

stat.CO2024★ 2 cited

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…