Data-driven uncertainty quantification in computational human head models

Computational models of the human head are promising tools for estimating the impact-induced response of the brain, and thus play an important role in the prediction of traumatic brain injury. The basic constituents of these models (i.e., model geometry, material properties, and boundary conditions)...

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Vydané v:	Computer methods in applied mechanics and engineering Ročník 398; s. 115108
Hlavní autori:	Upadhyay, Kshitiz, Giovanis, Dimitris G., Alshareef, Ahmed, Knutsen, Andrew K., Johnson, Curtis L., Carass, Aaron, Bayly, Philip V., Shields, Michael D., Ramesh, K.T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.08.2022 Elsevier BV
Predmet:	Boundary conditions Brain Computational efficiency Computing costs Diffusion Estimation Gaussian process Gaussian process regression Grassmannian diffusion maps Head injuries Head injury model Machine learning Manifolds (mathematics) Mapping Material properties Simulation Solution space Surrogate model Traumatic brain injury Traumatic Brain Injury (TBI) Uncertainty Uncertainty quantification Variability Uncertainty quantification Grassmannian diffusion maps Gaussian process regression Head injury model Traumatic Brain Injury (TBI) Surrogate model
ISSN:	0045-7825, 1879-2138
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