# optimal experimental design

Wednesday, September 6, 2017 - 2:55pm - 3:30pm

Youssef Marzouk (Massachusetts Institute of Technology)

Many inverse problems may involve a large number of observations. Yet these observations are seldom equally informative; moreover, practical constraints on storage, communication, and computational costs may limit the number of observations that one wishes to employ. We introduce strategies for selecting subsets of the data that yield accurate approximations of the inverse solution. This goal can also be understood in terms of optimal experimental design.

Tuesday, January 26, 2016 - 10:15am - 11:05am

Didier Henrion (Centre National de la Recherche Scientifique (CNRS))

Optimal experimental design consists of choosing measurements to maximize the information or, equivalently, minimize noise. For linear regression, a popular criterion is D-optimality, which seeks to maximize the determinant of the information matrix. Maximization is with respect to the weights of a discrete measure whose atoms (measurement basis vectors) are given a priori. The information matrix contains moments of degree two of this measure, and

its inverse is the error covariance matrix. The resulting determinant

its inverse is the error covariance matrix. The resulting determinant