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dc.contributor.author
Sudret, Bruno
dc.date.accessioned
2021-07-05T10:47:23Z
dc.date.available
2021-07-05T09:55:06Z
dc.date.available
2021-07-05T10:47:23Z
dc.date.issued
2021-06-30
dc.identifier.uri
http://hdl.handle.net/20.500.11850/492981
dc.identifier.doi
10.3929/ethz-b-000492981
dc.description.abstract
Computational models, a.k.a. simulators, are used in all fields of engineering and applied sciences to help design and assess complex systems in silico. Advanced analyses such as optimization or uncertainty quantification, which require repeated runs by varying input parameters, cannot be carried out with brute force methods such as Monte Carlo simulation due to computational costs. Thus the recent development of surrogate models such as polynomial chaos expansions and Gaussian processes, among others. For so-called stochastic simulators used e.g.in epidemiology, mathematical finance or wind turbine design, there exists an intrinsic source of stochasticity on top of well-identified system parameters. As a consequence, for a given vector of inputs, repeated runs of the simulator (called replications) will provide different results, as opposed to the case of deterministic simulators. Consequently, for each input realization, the response is a random variable to be characterized. In this talk we present an overview of the literature devoted to building surrogate models of such simulators, which we call stochastic emulators. Then we focus on a recent approach based on generalized lambda distributions and polynomial chaos expansions. The approach can be used with or without replications, which brings efficiency and versatility. As an outlook, practical applications to sensitivity analysis will also be presented.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc/4.0/
dc.subject
uncertainty quantification
en_US
dc.subject
Surrogate models
en_US
dc.subject
Stochastic simulators
en_US
dc.title
Recent developments on surrogate models for stochastic simulators
en_US
dc.type
Other Conference Item
dc.rights.license
Creative Commons Attribution-NonCommercial 4.0 International
ethz.pages.start
U 19437
en_US
ethz.size
55 p.
en_US
ethz.event
4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNECOMP 2021)
en_US
ethz.event.location
Athens, Greece
en_US
ethz.event.date
June 28–30, 2021
en_US
ethz.notes
Conference lecture held on June 30, 2021
en_US
ethz.grant
Surrogate Modelling for Stochastic Simulators (SAMOS)
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02605 - Institut für Baustatik u. Konstruktion / Institute of Structural Engineering::03962 - Sudret, Bruno / Sudret, Bruno
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02605 - Institut für Baustatik u. Konstruktion / Institute of Structural Engineering::03962 - Sudret, Bruno / Sudret, Bruno
en_US
ethz.grant.agreementno
175524
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projekte MINT
ethz.date.deposited
2021-07-05T09:55:11Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-07-05T10:47:43Z
ethz.rosetta.lastUpdated
2022-03-29T10:15:18Z
ethz.rosetta.versionExported
true
ethz.COinS
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