Bayesian uncertainty quantification for machine-learned models in physics

dc.creator	Gal Y., Koumoutsakos P., Lanusse F., Louppe G., Papadimitriou C.	en
dc.date.accessioned	2023-01-31T07:39:25Z
dc.date.available	2023-01-31T07:39:25Z
dc.date.issued	2022
dc.identifier	10.1038/s42254-022-00498-4
dc.identifier.issn	25225820
dc.identifier.uri	http://hdl.handle.net/11615/71900
dc.description.abstract	Being able to quantify uncertainty when comparing a theoretical or computational model to observations is critical to conducting a sound scientific investigation. With the rise of data-driven modelling, understanding various sources of uncertainty and developing methods to estimate them has gained renewed attention. Five researchers discuss uncertainty quantification in machine-learned models with an emphasis on issues relevant to physics problems. © 2022, Springer Nature Limited.	en
dc.language.iso	en	en
dc.source	Nature Reviews Physics	en
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137996919&doi=10.1038%2fs42254-022-00498-4&partnerID=40&md5=d21249e82d3ae75fb73619017fc481f8
dc.subject	Bayesian	en
dc.subject	Computational modelling	en
dc.subject	Data-driven model	en
dc.subject	Model understanding	en
dc.subject	Scientific investigation	en
dc.subject	Sources of uncertainty	en
dc.subject	Theoretical modeling	en
dc.subject	Uncertainty	en
dc.subject	Uncertainty quantifications	en
dc.subject	Uncertainty analysis	en
dc.subject	Springer Nature	en
dc.title	Bayesian uncertainty quantification for machine-learned models in physics	en
dc.type	journalArticle	en

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