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dc.contributor.authorBeck, Joakim
dc.contributor.authorDia, Ben Mansour
dc.contributor.authorEspath, Luis
dc.contributor.authorLong, Quan
dc.contributor.authorTempone, Raul
dc.identifier.citationBeck J, Dia BM, Espath LFR, Long Q, Tempone R (2018) Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain. Computer Methods in Applied Mechanics and Engineering 334: 523–553. Available:
dc.description.abstractIn calculating expected information gain in optimal Bayesian experimental design, the computation of the inner loop in the classical double-loop Monte Carlo requires a large number of samples and suffers from underflow if the number of samples is small. These drawbacks can be avoided by using an importance sampling approach. We present a computationally efficient method for optimal Bayesian experimental design that introduces importance sampling based on the Laplace method to the inner loop. We derive the optimal values for the method parameters in which the average computational cost is minimized for a specified error tolerance. We use three numerical examples to demonstrate the computational efficiency of our method compared with the classical double-loop Monte Carlo, and a single-loop Monte Carlo method that uses the Laplace approximation of the return value of the inner loop. The first demonstration example is a scalar problem that is linear in the uncertain parameter. The second example is a nonlinear scalar problem. The third example deals with the optimal sensor placement for an electrical impedance tomography experiment to recover the fiber orientation in laminate composites.
dc.description.sponsorshipThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST); KAUST CRG3 Award Ref:2281 and the KAUST CRG4 Award Ref:2584.
dc.publisherElsevier BV
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Computer Methods in Applied Mechanics and Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Methods in Applied Mechanics and Engineering, [, , (2018-02-19)] DOI: 10.1016/j.cma.2018.01.053 . © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
dc.subjectBayesian experimental design
dc.subjectExpected information gain
dc.subjectMonte Carlo
dc.subjectLaplace approximation
dc.subjectImportance sampling
dc.subjectComposite materials
dc.titleFast Bayesian experimental design: Laplace-based importance sampling for the expected information gain
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.identifier.journalComputer Methods in Applied Mechanics and Engineering
dc.contributor.institutionCenter for Integrative Petroleum Research (CIPR), College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia
dc.contributor.institutionUnited Technologies Research Center, East Hartford, CT, 06108, United States
kaust.personBeck, Joakim
kaust.personEspath, Luis
kaust.personTempone, Raul
kaust.grant.numberCRG3 Award Ref:2281
kaust.grant.numberCRG4 Award Ref:2584

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