Show simple item record

dc.contributor.authorvan Bloemen Waanders, B.
dc.contributor.authorFrangos, Michalis
dc.contributor.authorMarzouk, Youssef M
dc.contributor.authorWillcox, Karen E
dc.date.accessioned2016-12-01T19:55:14Z
dc.date.available2016-12-01T19:55:14Z
dc.date.issued2010-01
dc.identifier.isbn9780470697436
dc.identifier.isbn9780470685853
dc.identifier.urihttp://hdl.handle.net/1721.1/105500
dc.description.abstractSolution of statistical inverse problems via the frequentist or Bayesian approaches described in earlier chapters can be a computationally intensive endeavor, particularly when faced with large-scale forward models characteristic of many engineering and science applications. High computational cost arises in several ways. First, thousands or millions of forward simulations may be required to evaluate estimators of interest or to characterize a posterior distribution. In the large-scale setting, performing so many forward simulations is often computationally intractable. Second, sampling may be complicated by the large dimensionality of the input space--as when the inputs are fields represented with spatial discretizations of high dimension--and by nonlinear forward dynamics that lead to multimodal, skewed, and/or strongly correlated posteriors. In this chapter, we present an overview of surrogate and reduced order modeling methods that address these computational challenges. For illustration, we consider a Bayesian formulation of the inverse problem. Though some of the methods we review exploit prior information, they largely focus on simplifying or accelerating evaluations of a stochastic model for the data, and thus are also applicable in a frequentist context.en_US
dc.description.sponsorshipSandia National Laboratories (Laboratory Directed Research and Development (LDRD) program)en_US
dc.description.sponsorshipUnited States. Dept. of Energy (Contract DE-AC04-94AL85000)en_US
dc.description.sponsorshipSingapore-MIT Alliance Computational Engineering Programmeen_US
dc.description.sponsorshipUnited States. Dept. of Energy (Award Number DE-FG02-08ER25858 )en_US
dc.description.sponsorshipUnited States. Dept. of Energy (Award Number DESC00025217)en_US
dc.language.isoen_US
dc.publisherJohn Wiley & Sonsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1002/9780470685853en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceProf. Willcox via Barbara Williamsen_US
dc.titleSurrogate and reduced-order modeling: a comparison of approaches for large-scale statistical inverse problems [Chapter 7]en_US
dc.typeArticleen_US
dc.identifier.citationFrangos, M., Y. Marzouk, K. Willcox, and B. van Bloemen Waanders (2010). Surrogate and reduced-order modeling: a comparison of approaches for large-scale statistical inverse problems. In Lorenz Biegler, George Biros, Omar Ghattas, Matthias Heinkenschloss, David Keyes, Bani Mallick, Youssef Marzouk, Luis Tenorio, Bart van Bloemen Waanders, and Karen Willcox, eds., Large-scale inverse problems and quantification of uncertainty (pp. 123-149) New York: Wiley.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.approverWillcox, Karen Een_US
dc.contributor.mitauthorFrangos, Michalis
dc.contributor.mitauthorMarzouk, Youssef M
dc.contributor.mitauthorWillcox, Karen E
dc.relation.journalComputational Methods for Large-Scale Inverse Problems and Quantification of Uncertainty , Biegler et al. (Eds.)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/BookItemen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsFrangos, M.; Marzouk, Y.; Willcox, K.; van Bloemen Waanders, B.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-8242-3290
dc.identifier.orcidhttps://orcid.org/0000-0003-2156-9338
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record