FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3121034766> ?p ?o ?g. }

• W3121034766 abstract "Computational fluid dynamics plays a key role in the design process across many industries. Recently, there has been increasing interest in data-driven methods, in order to exploit the large volume of data generated by such computations. This paper introduces embedded Gaussian ridge functions, for rapid flowfield predictions. Gaussian ridge functions, which involve fitting a Gaussian process over a dimension reducing subspace, are obtained for numerous points within training flowfields. The functions can then be used to predict flow variables for new, previously unseen, flowfields. Their dimension reducing nature alleviates the problems associated with visualising high dimensional datasets, enabling improved understanding of design spaces and potentially providing valuable physical insights. A training and prediction framework is proposed, and demonstrated on the incompressible flow around a set of aerofoils. The framework is computationally efficient; consisting of either heavily parallelizable tasks, or linear algebra operations. To further reduce the computational cost, the computational grid is randomly subsampled, and ridge functions are obtained only at the sampled points. The flow physics encoded within covariance matrices obtained from the training flowfields is explored, and it is found that only a number of the leading modes are required to capture most of the relevant physics. This physics can be used to predict flow quantities, conditional upon those predicted by the ridge functions at the sampled points. This enables full flowfield predictions to be obtained, despite only having ridge functions at a small number of sample points. The resulting flowfield predictions are found to be competitive with those given by a state-of-the-art convolutional neural network trained on the same data. The underlying Gaussian processes allow for principled uncertainty quantification. Their posterior variance is incorporated into the covariance matrices, resulting in the upsampled flowfield predictions falling back on prior knowledge when predictive uncertainty is high. The end user can also view this uncertainty, giving them increased confidence in predictions. Additionally, this the possibility of including the CFD uncertainties within the framework exists, allowing for uncertainties in the CFD training data to be accounted for in the frameworks final predictions." @default.
• W3121034766 created "2021-01-18" @default.
• W3121034766 creator A5010856175 @default.
• W3121034766 creator A5019823984 @default.
• W3121034766 creator A5060993521 @default.
• W3121034766 date "2021-01-04" @default.
• W3121034766 modified "2023-10-18" @default.
• W3121034766 title "Instantaneous Flowfield Estimation with Gaussian Ridges" @default.
• W3121034766 cites W1502922572 @default.
• W3121034766 cites W1603452536 @default.
• W3121034766 cites W1901129140 @default.
• W3121034766 cites W1976502092 @default.
• W3121034766 cites W2046033161 @default.
• W3121034766 cites W2216523920 @default.
• W3121034766 cites W2246232460 @default.
• W3121034766 cites W2324881822 @default.
• W3121034766 cites W2466432219 @default.
• W3121034766 cites W2515505748 @default.
• W3121034766 cites W2742269066 @default.
• W3121034766 cites W2745110207 @default.
• W3121034766 cites W2748111298 @default.
• W3121034766 cites W2764024122 @default.
• W3121034766 cites W2795982117 @default.
• W3121034766 cites W2801938748 @default.
• W3121034766 cites W2906960111 @default.
• W3121034766 cites W2919874940 @default.
• W3121034766 cites W2945475836 @default.
• W3121034766 cites W2985630280 @default.
• W3121034766 cites W2988301498 @default.
• W3121034766 cites W3034037399 @default.
• W3121034766 cites W3046149134 @default.
• W3121034766 cites W3099878876 @default.
• W3121034766 cites W3125747271 @default.
• W3121034766 cites W4231758028 @default.
• W3121034766 cites W4252539427 @default.
• W3121034766 cites W4252750892 @default.
• W3121034766 cites W4302590514 @default.
• W3121034766 cites W575374134 @default.
• W3121034766 doi "https://doi.org/10.2514/6.2021-1138" @default.
• W3121034766 hasPublicationYear "2021" @default.
• W3121034766 type Work @default.
• W3121034766 sameAs 3121034766 @default.
• W3121034766 citedByCount "3" @default.
• W3121034766 countsByYear W31210347662021 @default.
• W3121034766 countsByYear W31210347662022 @default.
• W3121034766 countsByYear W31210347662023 @default.
• W3121034766 crossrefType "proceedings-article" @default.
• W3121034766 hasAuthorship W3121034766A5010856175 @default.
• W3121034766 hasAuthorship W3121034766A5019823984 @default.
• W3121034766 hasAuthorship W3121034766A5060993521 @default.
• W3121034766 hasConcept C105795698 @default.
• W3121034766 hasConcept C11413529 @default.
• W3121034766 hasConcept C121332964 @default.
• W3121034766 hasConcept C126255220 @default.
• W3121034766 hasConcept C127313418 @default.
• W3121034766 hasConcept C127413603 @default.
• W3121034766 hasConcept C146978453 @default.
• W3121034766 hasConcept C151730666 @default.
• W3121034766 hasConcept C1633027 @default.
• W3121034766 hasConcept C163716315 @default.
• W3121034766 hasConcept C178650346 @default.
• W3121034766 hasConcept C202444582 @default.
• W3121034766 hasConcept C2524010 @default.
• W3121034766 hasConcept C28826006 @default.
• W3121034766 hasConcept C32277403 @default.
• W3121034766 hasConcept C33676613 @default.
• W3121034766 hasConcept C33923547 @default.
• W3121034766 hasConcept C38349280 @default.
• W3121034766 hasConcept C41008148 @default.
• W3121034766 hasConcept C45374587 @default.
• W3121034766 hasConcept C5192115 @default.
• W3121034766 hasConcept C61326573 @default.
• W3121034766 hasConcept C62520636 @default.
• W3121034766 hasConcept C84655787 @default.
• W3121034766 hasConceptScore W3121034766C105795698 @default.
• W3121034766 hasConceptScore W3121034766C11413529 @default.
• W3121034766 hasConceptScore W3121034766C121332964 @default.
• W3121034766 hasConceptScore W3121034766C126255220 @default.
• W3121034766 hasConceptScore W3121034766C127313418 @default.
• W3121034766 hasConceptScore W3121034766C127413603 @default.
• W3121034766 hasConceptScore W3121034766C146978453 @default.
• W3121034766 hasConceptScore W3121034766C151730666 @default.
• W3121034766 hasConceptScore W3121034766C1633027 @default.
• W3121034766 hasConceptScore W3121034766C163716315 @default.
• W3121034766 hasConceptScore W3121034766C178650346 @default.
• W3121034766 hasConceptScore W3121034766C202444582 @default.
• W3121034766 hasConceptScore W3121034766C2524010 @default.
• W3121034766 hasConceptScore W3121034766C28826006 @default.
• W3121034766 hasConceptScore W3121034766C32277403 @default.
• W3121034766 hasConceptScore W3121034766C33676613 @default.
• W3121034766 hasConceptScore W3121034766C33923547 @default.
• W3121034766 hasConceptScore W3121034766C38349280 @default.
• W3121034766 hasConceptScore W3121034766C41008148 @default.
• W3121034766 hasConceptScore W3121034766C45374587 @default.
• W3121034766 hasConceptScore W3121034766C5192115 @default.
• W3121034766 hasConceptScore W3121034766C61326573 @default.
• W3121034766 hasConceptScore W3121034766C62520636 @default.
• W3121034766 hasConceptScore W3121034766C84655787 @default.
• W3121034766 hasLocation W31210347661 @default.
• W3121034766 hasOpenAccess W3121034766 @default.

• next