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Exploring multi-fidelity noisy data: Methods and real-world examples
dc.contributor.author
Giannoukou, Katerina
dc.contributor.author
Ascia, Paolo
dc.contributor.author
Marelli, Stefano
dc.contributor.author
Duddeck, Fabian
dc.contributor.author
Sudret, Bruno
dc.date.accessioned
2024-10-02T11:11:30Z
dc.date.available
2024-09-27T16:45:24Z
dc.date.available
2024-09-30T15:17:44Z
dc.date.available
2024-10-02T11:11:30Z
dc.date.issued
2024
dc.identifier.uri
http://hdl.handle.net/20.500.11850/696737
dc.description.abstract
Multi-fidelity surrogate models (MFSMs) are a well-established tool to combine information from sources with diverse computational fidelities into a single surrogate model. The sources of higher or lower fidelity can be, for example, computer simulations or physical experiments. MFSMs can exhibit enhanced predictive accuracy and reduced costs in emulating the response of complex systems, outperforming their single-fidelity surrogate model counterparts at comparable training costs. In real-world applications, uncertainty is present in the data, regardless of their fidelity. This uncertainty can be due to measurement noise, numerical noise, or unobserved/latent variables, and adds a layer of complexity by introducing non-deterministic behavior in the system response. In this work, we provide a framework to address the uncertainty in MFSM scenarios. The effectiveness of our approach is demonstrated through a transfer learning application in crashworthiness and a real-world wind turbine application, showcasing the applicability and versatility of our proposed methods.
en_US
dc.language.iso
en
en_US
dc.title
Exploring multi-fidelity noisy data: Methods and real-world examples
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dc.type
Conference Paper
ethz.event
31st International Conference on Noise and Vibration Engineering (ISMA2024) and 10th International Conference on Uncertainty in Structural Dynamics (USD2024)
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ethz.event.location
Leuven, Belgium
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ethz.event.date
September 9-11, 2024
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ethz.notes
Presentation held on September 11, 2024
en_US
ethz.grant
European Training Network on Grey-Box Models for Safe and Reliable Intelligent Mobility Systems
en_US
ethz.publication.status
accepted
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
955393
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
H2020
ethz.date.deposited
2024-09-27T16:45:24Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.exportRequired
true
ethz.COinS
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Conference Paper [35619]