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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Popis
Shrnutí:	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) are often associated with significant uncertainty and variability. As a result, uncertainty quantification (UQ), which involves quantification of the effect of this uncertainty and variability on the simulated response, becomes critical to ensure reliability of model predictions. Modern biofidelic head model simulations are associated with very high computational cost and high-dimensional inputs and outputs, which limits the applicability of traditional UQ methods on these systems. In this study, a two-stage, data-driven manifold learning-based framework is proposed for UQ of computational head models. This framework is demonstrated on a 2D subject-specific head model, where the goal is to quantify uncertainty in the simulated strain fields (i.e., output), given variability in the material properties of different brain substructures (i.e., input). In the first stage, a data-driven method based on multi-dimensional Gaussian kernel-density estimation and diffusion maps is used to generate realizations of the input random vector directly from the available data. Computational simulations of a small number of realizations provide input–output pairs for training data-driven surrogate models in the second stage. The surrogate models employ nonlinear dimensionality reduction using Grassmannian diffusion maps, Gaussian process regression to create a low-cost mapping between the input random vector and the reduced solution space, and geometric harmonics models for mapping between the reduced space and the Grassmann manifold. It is demonstrated that the surrogate models provide highly accurate approximations of the computational model while significantly reducing the computational cost. Monte Carlo simulations of the surrogate models are used for uncertainty propagation. UQ of the strain fields highlights significant spatial variation in model uncertainty, and reveals key differences in uncertainty among commonly used strain-based brain injury predictor variables.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0045-7825 1879-2138
DOI:	10.1016/j.cma.2022.115108