Data-driven Uncertainty Quantification in Computational Human Head Models.
Data-driven Uncertainty Quantification in Computational Human Head Models.
复制标题
计算人体头部模型中数据驱动的不确定性量化。
DOI：
10.1016/j.cma.2022.115108
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发表时间：
2022
影响因子：
7.2
通讯作者：
Ramesh,KT
中科院分区：
文献类型：
--
作者：
Upadhyay,Kshitiz;Giovanis,DimitrisG;Alshareef,Ahmed;Knutsen,AndrewK;Johnson,CurtisL;Carass,Aaron;Bayly,PhilipV;Shields,MichaelD;Ramesh,KT
人脑的计算模型是一种很有前途的工具，用于估计大脑的冲击诱导反应，因此在预测创伤性脑损伤方面发挥着重要作用。这些模型的基本组成部分（即，模型几何、材料属性和边界条件）通常与显著的不确定性和可变性相关联。因此，不确定性量化（UQ），包括对这种不确定性和可变性对模拟响应的影响的量化，对于确保模型预测的可靠性至关重要。现代生物头部模型模拟具有很高的计算成本和高维的输入和输出，这限制了传统UQ方法在这些系统中的适用性。在这项研究中，提出了一个两阶段的，基于数据驱动的流形学习的计算头部模型的UQ框架。该框架在2D受试者特定头部模型上进行了演示，其目标是量化模拟应变场（即输出）的不确定性，给定不同大脑子结构（即输入）的材料特性的可变性。在第一阶段，使用基于多维高斯核密度估计和扩散映射的数据驱动方法直接从可用数据中生成输入随机向量的实现。在第二阶段，少量实现的计算模拟为训练数据驱动的代理模型提供了输入-输出对。代理模型采用非线性降维，使用格拉斯曼扩散映射，高斯过程回归在输入随机向量和约简解空间之间创建低成本映射，以及几何谐波模型在约简空间和格拉斯曼流形之间进行映射。结果表明，代理模型提供了计算模型的高精度近似值，同时显著降低了计算成本。不确定性传播采用代理模型的蒙特卡罗模拟。应变场的UQ突出了模型不确定性的显著空间差异，揭示了常用的基于应变的脑损伤预测变量在不确定性方面的关键差异。
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.
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DOI：
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