Stochastic Optimization for Design of Experiments

André G. Carlon, Rafael H. Lopez, Luis Espath

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a stochastic optimization method to converge to local maxima of the Shannon's expected information gain of experiments using a Bayesian framework. We avoid the high cost of evaluating several double loop Monte Carlo simulation (DLMC) each iteration by employing the stochastic gradient. Our method has proven to converge to local maxima with a fraction of the cost of the classical approach, making possible to optimize experiments with more expensive models. We confirm the convergence of our method optimizing experimental design problems with analytical ODE models and with finite elements approximations of PDEs.
Original languageEnglish (US)
Title of host publicationProceedings of the XXXVIII Iberian Latin American Congress on Computational Methods in Engineering
PublisherABMEC Brazilian Association of Computational Methods in Engineering
StatePublished - 2017

Bibliographical note

KAUST Repository Item: Exported on 2021-04-06


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