可协调的非稳态过程

IF 2.1 3区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-07-30 DOI:10.1137/22m1544580
Mircea Grigoriu
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引用次数: 0

摘要

SIAM/ASA 不确定性量化期刊》,第 12 卷第 3 期,第 842-867 页,2024 年 9 月。 摘要.可调和过程可以用具有随机系数的谐波之和表示,这些系数是相关的,而不是像弱静止过程那样是不相关的。可调和过程在第二矩意义上的特征是其广义谱密度函数。研究表明,可调和过程允许频谱表示,并且可以是带限制的和/或窄带的;可调和高斯过程的样本可以通过类似于生成静态高斯过程样本的算法生成;可调和过程可以构建精确的有限维(FD)代理变量,即时间的确定性函数和随机变量的有限集;在温和条件下,各种非静态过程都是可调和的。本报告通过数字示例,包括各种非平稳过程和线性系统对随机输入的输出,展示了可协调过程的多功能性。
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Harmonizable Nonstationary Processes
SIAM/ASA Journal on Uncertainty Quantification, Volume 12, Issue 3, Page 842-867, September 2024.
Abstract.Harmonizable processes can be represented by sums of harmonics with random coefficients, which are correlated rather than uncorrelated as for weakly stationary processes. Harmonizable processes are characterized in the second moment sense by their generalized spectral density functions. It is shown that harmonizable processes admit spectral representations and can be band limited and/or narrow band; samples of harmonizable Gaussian processes can be generated by algorithms similar to those used to generate samples of stationary Gaussian processes; accurate finite dimensional (FD) surrogates, i.e., deterministic functions of time and finite sets of random variables, can be constructed for harmonizable processes; and, under mild conditions, a broad range of nonstationary processes are harmonizable. Numerical illustrations, including various nonstationary processes and outputs of linear systems to random inputs, are presented to demonstrate the versatility of harmonizable processes.
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来源期刊
Siam-Asa Journal on Uncertainty Quantification
Siam-Asa Journal on Uncertainty Quantification Mathematics-Statistics and Probability
CiteScore
3.70
自引率
0.00%
发文量
51
期刊介绍: SIAM/ASA Journal on Uncertainty Quantification (JUQ) publishes research articles presenting significant mathematical, statistical, algorithmic, and application advances in uncertainty quantification, defined as the interface of complex modeling of processes and data, especially characterizations of the uncertainties inherent in the use of such models. The journal also focuses on related fields such as sensitivity analysis, model validation, model calibration, data assimilation, and code verification. The journal also solicits papers describing new ideas that could lead to significant progress in methodology for uncertainty quantification as well as review articles on particular aspects. The journal is dedicated to nurturing synergistic interactions between the mathematical, statistical, computational, and applications communities involved in uncertainty quantification and related areas. JUQ is jointly offered by SIAM and the American Statistical Association.
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