Stochastic Optimization for Design of Experiments

Abstract
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.

Citation
Carlon, A. G., Lopez, R. H., & Espath, L. F. da R. (2017). Stochastic Optimization for Design of Experiments. Proceedings of the XXXVIII Iberian Latin American Congress on Computational Methods in Engineering. doi:10.20906/cps/cilamce2017-0149

Publisher
SWGE Sistemas

Conference/Event Name
XXXVIII Ibero-Latin American Congress on Computational Methods in Engineering

DOI
10.20906/cps/cilamce2017-0149

Additional Links
http://www.swge.inf.br/proceedings/paper/?P=CILAMCE2017-0149

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