Finite dimensional models for random functions
Finite dimensional models for random functions
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DOI：
10.1016/j.jcp.2018.09.029
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
M. Grigoriu
中科院分区：
文献类型：
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作者：
M. Grigoriu
截断的Karhunen-Loève（KL）表示用于构造具有有限方差的非高斯函数的有限维（FD）模型。这些表示的随机系数的二阶矩规范增强到全概率特性通过使用翻译，多项式混沌，翻译多项式混沌模型，被称为T，PC和PCT模型。KL表示和T，PC和PCT模型的理论考虑，三个数值例子来说明这些模型的实现和性能。PCT模型继承了T和PC模型的理想特征。它精确地近似了这些例子中考虑的所有感兴趣的量。
Truncated Karhunen–Loève (KL) representations are used to construct finite dimensional (FD) models for non-Gaussian functions with finite variances. The second moment specification of the random coefficients of these representations are enhanced to full probabilistic characterization by using translation, polynomial chaos, and translated polynomial chaos models, referred to as T, PC, and PCT models. Following theoretical considerations on KL representations and T, PC, and PCT models, three numerical examples are presented to illustrate the implementation and performance of these models. The PCT models inherit the desirable features of both T and PC models. It approximates accurately all quantities of interest considered in these examples.