FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2909382383> ?p ?o ?g. }

• W2909382383 endingPage "72" @default.
• W2909382383 startingPage "66" @default.
• W2909382383 abstract "In this work, we demonstrate that machine learning methods can be reliably used to predict radiative properties of dispersed media, i.e. packed beds, as a function of packed bed geometry and material properties. The computationally expensive Monte Carlo ray tracing (MCRT) method, which is widely used in this context, is replaced by Neural Networks (NN). We demonstrate that the data-driven surrogate prediction works accurately and generally. The results of both MCRT and NN models agree well with each other and with previously measured literature results. We also measure the uncertainty of the NN results using statistical methods. It is recommended that the developed model be used for efficient inverse problems and optimizations in relevant future work." @default.
• W2909382383 created "2019-01-25" @default.
• W2909382383 creator A5033835217 @default.
• W2909382383 creator A5035037725 @default.
• W2909382383 creator A5071505267 @default.
• W2909382383 date "2019-03-01" @default.
• W2909382383 modified "2023-10-04" @default.
• W2909382383 title "A data driven artificial neural network model for predicting radiative properties of metallic packed beds" @default.
• W2909382383 cites W1964639562 @default.
• W2909382383 cites W1984753492 @default.
• W2909382383 cites W1988696933 @default.
• W2909382383 cites W1990131553 @default.
• W2909382383 cites W2002316239 @default.
• W2909382383 cites W2011614774 @default.
• W2909382383 cites W2020380120 @default.
• W2909382383 cites W2022965856 @default.
• W2909382383 cites W2030132496 @default.
• W2909382383 cites W2034197694 @default.
• W2909382383 cites W2038823753 @default.
• W2909382383 cites W2040092340 @default.
• W2909382383 cites W2075718677 @default.
• W2909382383 cites W2085844219 @default.
• W2909382383 cites W2090075409 @default.
• W2909382383 cites W2095482238 @default.
• W2909382383 cites W2102257123 @default.
• W2909382383 cites W2111051539 @default.
• W2909382383 cites W2140191942 @default.
• W2909382383 cites W2151995699 @default.
• W2909382383 cites W2160260133 @default.
• W2909382383 cites W2171994375 @default.
• W2909382383 cites W2466100828 @default.
• W2909382383 cites W2608586253 @default.
• W2909382383 cites W2769109837 @default.
• W2909382383 cites W2773646530 @default.
• W2909382383 cites W2792305278 @default.
• W2909382383 cites W2806737434 @default.
• W2909382383 cites W3103362336 @default.
• W2909382383 cites W871063969 @default.
• W2909382383 doi "https://doi.org/10.1016/j.jqsrt.2019.01.013" @default.
• W2909382383 hasPublicationYear "2019" @default.
• W2909382383 type Work @default.
• W2909382383 sameAs 2909382383 @default.
• W2909382383 citedByCount "24" @default.
• W2909382383 countsByYear W29093823832019 @default.
• W2909382383 countsByYear W29093823832020 @default.
• W2909382383 countsByYear W29093823832021 @default.
• W2909382383 countsByYear W29093823832022 @default.
• W2909382383 countsByYear W29093823832023 @default.
• W2909382383 crossrefType "journal-article" @default.
• W2909382383 hasAuthorship W2909382383A5033835217 @default.
• W2909382383 hasAuthorship W2909382383A5035037725 @default.
• W2909382383 hasAuthorship W2909382383A5071505267 @default.
• W2909382383 hasConcept C105795698 @default.
• W2909382383 hasConcept C111919701 @default.
• W2909382383 hasConcept C119857082 @default.
• W2909382383 hasConcept C120665830 @default.
• W2909382383 hasConcept C121332964 @default.
• W2909382383 hasConcept C124101348 @default.
• W2909382383 hasConcept C127313418 @default.
• W2909382383 hasConcept C138673069 @default.
• W2909382383 hasConcept C14036430 @default.
• W2909382383 hasConcept C151730666 @default.
• W2909382383 hasConcept C172331833 @default.
• W2909382383 hasConcept C185592680 @default.
• W2909382383 hasConcept C18762648 @default.
• W2909382383 hasConcept C192562407 @default.
• W2909382383 hasConcept C19499675 @default.
• W2909382383 hasConcept C2779343474 @default.
• W2909382383 hasConcept C2780009758 @default.
• W2909382383 hasConcept C33923547 @default.
• W2909382383 hasConcept C41008148 @default.
• W2909382383 hasConcept C43617362 @default.
• W2909382383 hasConcept C50644808 @default.
• W2909382383 hasConcept C74902906 @default.
• W2909382383 hasConcept C78458016 @default.
• W2909382383 hasConcept C86803240 @default.
• W2909382383 hasConcept C97355855 @default.
• W2909382383 hasConceptScore W2909382383C105795698 @default.
• W2909382383 hasConceptScore W2909382383C111919701 @default.
• W2909382383 hasConceptScore W2909382383C119857082 @default.
• W2909382383 hasConceptScore W2909382383C120665830 @default.
• W2909382383 hasConceptScore W2909382383C121332964 @default.
• W2909382383 hasConceptScore W2909382383C124101348 @default.
• W2909382383 hasConceptScore W2909382383C127313418 @default.
• W2909382383 hasConceptScore W2909382383C138673069 @default.
• W2909382383 hasConceptScore W2909382383C14036430 @default.
• W2909382383 hasConceptScore W2909382383C151730666 @default.
• W2909382383 hasConceptScore W2909382383C172331833 @default.
• W2909382383 hasConceptScore W2909382383C185592680 @default.
• W2909382383 hasConceptScore W2909382383C18762648 @default.
• W2909382383 hasConceptScore W2909382383C192562407 @default.
• W2909382383 hasConceptScore W2909382383C19499675 @default.
• W2909382383 hasConceptScore W2909382383C2779343474 @default.
• W2909382383 hasConceptScore W2909382383C2780009758 @default.
• W2909382383 hasConceptScore W2909382383C33923547 @default.
• W2909382383 hasConceptScore W2909382383C41008148 @default.
• W2909382383 hasConceptScore W2909382383C43617362 @default.
• W2909382383 hasConceptScore W2909382383C50644808 @default.

• next