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dc.contributor.author
Sudret, Bruno
dc.date.accessioned
2021-07-06T04:15:41Z
dc.date.available
2021-07-05T14:08:29Z
dc.date.available
2021-07-06T04:15:41Z
dc.date.issued
2021-06-17
dc.identifier.uri
http://hdl.handle.net/20.500.11850/493130
dc.identifier.doi
10.3929/ethz-b-000493130
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, an intrinsic source of stochasticity exists 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 single input, 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.publisher
ETH Zurich, Chair of Risk, Safety and Uncertainty Quantification,
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc/4.0/
dc.subject
Uncertainty quantification
en_US
dc.subject
Stochastic simulators
en_US
dc.subject
Lambda distributions
en_US
dc.title
Surrogate modelling approaches for stochastic simulators
en_US
dc.type
Presentation
dc.rights.license
Creative Commons Attribution-NonCommercial 4.0 International
ethz.size
66 p.
en_US
ethz.event
Seminar for machine learning and uncertainty quantification in scientific computing at the Centrum Wiskunde & Informatica
en_US
ethz.event.location
Amsterdam, The Netherlands
en_US
ethz.event.date
June 17, 2021
en_US
ethz.grant
Surrogate Modelling for Stochastic Simulators (SAMOS)
en_US
ethz.publication.place
Zurich
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
Projektförderung in Mathematik, Natur- und Ingenieurwissenschaften (Abteilung II)
ethz.relation.cites
10.3929/ethz-b-000394777
ethz.date.deposited
2021-07-05T14:08:37Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-07-06T04:16:18Z
ethz.rosetta.lastUpdated
2021-07-06T04:16:18Z
ethz.rosetta.versionExported
true
ethz.COinS
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