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 |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |