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  Multi-fidelity Gaussian process surrogate modeling for regression problems in physics

Ravi, K., Fediukov, V., Dietrich, F., Neckel, T., Buse, F., Bergmann, M., et al. (2024). Multi-fidelity Gaussian process surrogate modeling for regression problems in physics. Machine Learning: Science and Technology, 5: 045015. doi:10.1088/2632-2153/ad7ad5.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000F-F6A9-D Version Permalink: https://hdl.handle.net/21.11116/0000-000F-F6AA-C
Genre: Journal Article

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ravi_multi-fidelity.pdf (Supplementary material), 3MB
 
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https://doi.org/10.1088/2632-2153/ad7ad5 (Publisher version)
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 Creators:
Ravi, K.1, Author
Fediukov, V.1, Author
Dietrich, F.1, Author
Neckel, T.1, Author
Buse, F.1, Author
Bergmann, M.2, Author                 
Bungartz, H.-J.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Tokamak Scenario Development (E1), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856321              

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Language(s): eng - English
 Dates: 2024
 Publication Status: Published online
 Pages: 26 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1088/2632-2153/ad7ad5
 Degree: -

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Title: Machine Learning: Science and Technology
  Abbreviation : Mach. Learn.: Sci. Technol.
Source Genre: Journal
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Publ. Info: Bristol, UK : IOP Publishing
Pages: - Volume / Issue: 5 Sequence Number: 045015 Start / End Page: - Identifier: ISSN: 2632-2153
CoNE: https://pure.mpg.de/cone/journals/resource/2632-2153