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dc.contributor.authorLykkegaard, MB
dc.contributor.authorDodwell, TJ
dc.contributor.authorMoxey, D
dc.date.accessioned2021-05-18T09:12:13Z
dc.date.issued2021-05-18
dc.description.abstractThis is the code used for the Lykkegaard et al. (2021) article "Accelerating uncertainty quantification of groundwater flow modelling using a deep neural network proxy" published in Computer Methods in Applied Mechanics and Engineering.en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.description.sponsorshipAlan Turing Instituteen_GB
dc.identifier.grantnumberEP/L016214/1en_GB
dc.identifier.grantnumberEP/R029423/1en_GB
dc.identifier.grantnumber2TAFFP\100007en_GB
dc.identifier.urihttp://hdl.handle.net/10871/125733
dc.language.isoenen_GB
dc.publisherUniversity of Exeteren_GB
dc.relation.urlhttp://hdl.handle.net/10871/125704en_GB
dc.rightsGNU General Public Licence 3.0en_GB
dc.subjectGroundwater flowen_GB
dc.subjectUncertainty quantificationen_GB
dc.subjectMarkov chain Monte Carloen_GB
dc.subjectSurrogate modelsen_GB
dc.subjectDeep neural networksen_GB
dc.titleCode to reproduce results from "Accelerating uncertainty quantification of groundwater flow modelling using a deep neural network proxy"en_GB
dc.typeSoftwareen_GB
dc.date.available2021-09-01en_GB
dc.date.available2021-04-26en_GB
dc.date.available2021-05-18T09:12:13Z
pubs.notesNot knownen_GB
dc.descriptionThe article associated with this software is available in ORE at: http://hdl.handle.net/10871/125704en_GB
dc.rights.urihttps://choosealicense.com/licenses/gpl-3.0en_GB
exeter.funder::Engineering and Physical Sciences Research Council (EPSRC)en_GB
exeter.funder::Engineering and Physical Sciences Research Council (EPSRC)en_GB
exeter.funder::Alan Turing Instituteen_GB
rioxxterms.versionNAen_GB
rioxxterms.licenseref.startdate2021-05-18
rioxxterms.typeOtheren_GB
refterms.dateFOA2021-05-18T09:12:56Z


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