# Optimal Experimental Design for Infinite-dimensional Bayesian Inverse Problems Governed by PDEs: A Review

arXiv: https://arxiv.org/abs/2005.12998 · HTML: https://mlsanity.com/paper/2005.12998/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 1 of 1)

- [Stochastic Learning Approach to Binary Optimization for Optimal Design of Experiments](https://mlsanity.com/paper/2101.05958/) (2021)

## Abstract (arXiv)

We present a review of methods for optimal experimental design (OED) for Bayesian inverse problems governed by partial differential equations with infinite-dimensional parameters. The focus is on problems where one seeks to optimize the placement of measurement points, at which data are collected, such that the uncertainty in the estimated parameters is minimized. We present the mathematical foundations of OED in this context and survey the computational methods for the class of OED problems under study. We also outline some directions for future research in this area.