A generalized multi-resolution expansion for uncertainty propagation with application to cardiovascular modeling.
A generalized multi-resolution expansion for uncertainty propagation with application to cardiovascular modeling.
复制标题
不确定性传播的广义多分辨率扩展及其在心血管建模中的应用。
DOI：
10.1016/j.cma.2016.09.024
复制
发表时间：
2017
影响因子：
7.2
通讯作者：
Marsden,AL
中科院分区：
文献类型：
--
作者：
Schiavazzi,DE;Doostan,A;Iaccarino,G;Marsden,AL
计算模型用于各种领域，以提高我们对复杂物理现象的理解。最近，模型预测的真实性已大大提高了从确定性过渡到随机框架，在参数，载荷，本构特性，模型几何形状和其他数量的内在变化的影响可以更自然地包括在内。一般的随机系统可以由大量的任意分布和相关的随机输入，以及有限的支持响应与尖锐的梯度或事件不连续的特点。本文将以往提出的不确定性传播的多分辨率方法进行了推广，提出了一种提高计算效率，能够处理任意分布的随机输入和非光滑随机响应，并且自然地促进自适应性，即， 扩展系数对关于解细化的信息进行编码。我们的方法依赖于划分的随机空间的元素，细分沿着一个单一的维度，或者，换句话说，渐进的细化表现出二叉树表示。我们还展示了这些二进制改进如何特别有效地避免多分辨率基基数的指数增长，并显着降低中高维随机输入的回归复杂性。通过先前提出的不确定性传播基准和随机多尺度有限元模拟心血管血流的方法的性能证明。
Computational models are used in a variety of fields to improve our understanding of complex physical phenomena. Recently, the realism of model predictions has been greatly enhanced by transitioning from deterministic to stochastic frameworks, where the effects of the intrinsic variability in parameters, loads, constitutive properties, model geometry and other quantities can be more naturally included. A general stochastic system may be characterized by a large number of arbitrarily distributed and correlated random inputs, and a limited support response with sharp gradients or event discontinuities. This motivates continued research into novel adaptive algorithms for uncertainty propagation, particularly those handling high dimensional, arbitrarily distributed random inputs and non-smooth stochastic responses.In this work, we generalize a previously proposed multi-resolution approach to uncertainty propagation to develop a method that improves computational efficiency, can handle arbitrarily distributed random inputs and non-smooth stochastic responses, and naturally facilitates adaptivity, i.e., the expansion coefficients encode information on solution refinement. Our approach relies on partitioning the stochastic space into elements that are subdivided along a single dimension, or, in other words, progressive refinements exhibiting a binary tree representation. We also show how these binary refinements are particularly effective in avoiding the exponential increase in the multi-resolution basis cardinality and significantly reduce the regression complexity for moderate to high dimensional random inputs. The performance of the approach is demonstrated through previously proposed uncertainty propagation benchmarks and stochastic multi-scale finite element simulations in cardiovascular flow.
谷胱甘肽的功能：生化、生理、毒理学和临床方面
DOI：
--
发表时间：
1983
期刊：
影响因子：
--
作者：
A. Larsson
通讯作者：
A. Larsson
细胞抗氧化防御机制
DOI：
--
发表时间：
2019
期刊：
影响因子：
--
作者：
C. Chow
通讯作者：
C. Chow
影响因子：
64.8
作者：
DUMUIS, A;SEBBEN, M;BOCKAERT, J
通讯作者：
BOCKAERT, J