Bayesian Optimal Experimental Design

insight - Bayesian Optimal Experimental Design

Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo

Predictive goal-oriented OED seeks to maximize the expected information gain on quantities of interest, distinct from traditional parameter-focused OED.

Scalable and Efficient Bayesian Optimal Experimental Design with Derivative-Informed Neural Operators

The core message of this article is to develop an accurate, scalable, and efficient computational framework for Bayesian optimal experimental design (OED) problems by leveraging derivative-informed neural operators (DINOs). The proposed method addresses the key challenges in Bayesian OED, including the high computational cost of evaluating the parameter-to-observable (PtO) map and its derivative, the curse of dimensionality in the parameter and experimental design spaces, and the combinatorial optimization for sensor selection.

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