Show simple item record

dc.contributor.author
Sudret, Bruno
dc.date.accessioned
2019-08-21T12:19:25Z
dc.date.available
2019-08-21T10:43:31Z
dc.date.available
2019-08-21T11:01:25Z
dc.date.available
2019-08-21T11:05:45Z
dc.date.available
2019-08-21T12:19:25Z
dc.date.issued
2019-03-19
dc.identifier.uri
http://hdl.handle.net/20.500.11850/359614
dc.identifier.doi
10.3929/ethz-b-000359614
dc.description.abstract
The links between uncertainty quantification and classical machine learning (ML) have tightened in the past few years. On the one hand, uncertainty propagation techniques are nowadays mainly based on the use of surrogate models such as polynomial chaos expansions (PCE), Gaussian processes or low-rank tensor representations that are constructed in a non-intrusive manner using a batch of computer experiments and dedicated algorithms. On the other hand, in the context of supervised learning, artificial intelligence algorithms are used to build predictive models from input/output data sets. In this talk we will emphasize the links between the two fields, and present more specifically recent developments on sparse polynomial chaos expansions that allow one to handle problems with functional output quantities (e.g. trajectories of a dynamical system). As a second application, sparse PCEs will be used in a purely data-driven approach to handle benchmark data sets that are commonly used in the machine learning community. It is shown that sparse PCEs give as good as, or even better results than the (ad-hoc tuned) ML ones presented in the literature.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Machine learning
en_US
dc.subject
Polynomial chaos expansions
en_US
dc.subject
Uncertainty quantification
en_US
dc.title
Data-based sparse polynomial chaos expansions: applications in dynamics and machine learning
en_US
dc.type
Other Conference Item
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2019-08-21
ethz.size
70 slides
en_US
ethz.event
UQOP: Uncertainty Quantification and OPtimization
en_US
ethz.event.location
Paris, France
en_US
ethz.event.date
March 18-20, 2019
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.date.deposited
2019-08-21T10:43:41Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-08-21T11:01:43Z
ethz.rosetta.lastUpdated
2021-02-15T05:41:58Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Data-based%20sparse%20polynomial%20chaos%20expansions:%20applications%20in%20dynamics%20and%20machine%20learning&rft.date=2019-03-19&rft.au=Sudret,%20Bruno&rft.genre=unknown&rft.btitle=Data-based%20sparse%20polynomial%20chaos%20expansions:%20applications%20in%20dynamics%20and%20machine%20learning
 Search print copy at ETH Library

Files in this item

Thumbnail

Publication type

Show simple item record