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AuthorStenzel, Oledc.contributor.author
AuthorPecho, Omardc.contributor.author
AuthorHolzer, Lorenzdc.contributor.author
AuthorNeumann, Matthiasdc.contributor.author
AuthorSchmidt, Volkerdc.contributor.author
Date of accession2018-12-18T08:10:38Zdc.date.accessioned
Available in OPARU since2018-12-18T08:10:38Zdc.date.available
Date of first publication2017dc.date.issued
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
Keywordbig datadc.subject
Keywordeffective conductivitydc.subject
Keywordgeodesic tortuositydc.subject
Keywordmicrostructure characteristicsdc.subject
Keywordpredictive simulationdc.subject
Keywordstochastic microstructure modelingdc.subject
Keywordeffective transport-propertiesdc.subject
Keywordporedc.subject
Keywordmicroscopydc.subject
Keyworddiffusiondc.subject
Keywordsciencedc.subject
Keywordfuturedc.subject
Keywordcellsdc.subject
Dewey Decimal GroupDDC 510 / Mathematicsdc.subject.ddc
TitleBig Data for Microstructure-Property Relationships: A Case Study of Predicting Effective Conductivitiesdc.title
Resource typeWissenschaftlicher Artikeldc.type
FacultyFakultät für Mathematik und Wirtschaftswissenschaftenuulm.affiliationGeneral
InstitutionInstitut für Stochastikuulm.affiliationSpecific
DCMI TypeTextuulm.typeDCMI
CategoryPublikationsnachweiseuulm.category
DOI (external)10.1002/aic.15757dc.identifier.doiExternal
Source - Title of sourceAIChE Journalsource.title
Source - Place of publicationWileysource.publisher
Source - Volume63source.volume
Source - Issue9source.issue
Source - Year2017source.year
Source - From page4224source.fromPage
Source - To page4232source.toPage
Source - ISSN0001-1541source.identifier.issn
Source - eISSN1547-5905source.identifier.eissn
CommunityFakultät für Mathematik und Wirtschaftswissenschaftenuulm.community
WoS000407192500049uulm.identifier.wos
Bibliographyuulmuulm.bibliographie


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