Measuring inputs-outputs association for time-depending hazard models under safety objectives using kernels

IF 1.5 4区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY International Journal for Uncertainty Quantification Pub Date : 2024-03-01 DOI:10.1615/int.j.uncertaintyquantification.2024049119

Matieyendou LAMBONI

引用次数: 0

Abstract

A methodology for assessing the inputs-outputs association for time-depending predictive models under failure mode for instance is investigated. Firstly, new dependency models for sampling random values of uncertain inputs that comply with the safety objectives are provided. Secondly, the asymmetric role of outputs and inputs leads to develop new kernel-based statistical tests of independence between the inputs and outputs using the dependency models. The associated test statistics are normalized so as to introduce new kernel-based sensitivity indices (Kb-SIs). Such first-order and total Kb-SIs allow for i) assessing the inputs effects on the whole dynamic outputs subjected to safety objectives, ii) dealing with sensitivity functionals (SFs) having heavy-tailed distributions or non-stationary time-depending SFs thanks to kernel methods. Our approach is also well-suited for dynamic models with prescribed copulas of inputs.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

利用核素测量安全目标下随时间变化的危险模型的投入产出关联性

以故障模式为例，研究了一种评估时滞预测模型输入输出关联的方法。首先，为符合安全目标的不确定输入随机值采样提供了新的依赖模型。其次，由于输出和输入的非对称作用，利用依存模型开发了新的基于核的输入和输出独立性统计检验。对相关的测试统计量进行归一化处理，以引入新的基于内核的灵敏度指数（Kb-SIs）。这种一阶和总的 Kb-SIs 可用于 i) 评估输入对整个动态输出的影响，以达到安全目标；ii) 利用核方法处理具有重尾分布或非平稳的随时间变化的灵敏度函数（SF）。我们的方法也非常适合具有规定输入协方差的动态模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

International Journal for Uncertainty Quantification ENGINEERING, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.60

自引率

5.90%

发文量

期刊介绍： The International Journal for Uncertainty Quantification disseminates information of permanent interest in the areas of analysis, modeling, design and control of complex systems in the presence of uncertainty. The journal seeks to emphasize methods that cross stochastic analysis, statistical modeling and scientific computing. Systems of interest are governed by differential equations possibly with multiscale features. Topics of particular interest include representation of uncertainty, propagation of uncertainty across scales, resolving the curse of dimensionality, long-time integration for stochastic PDEs, data-driven approaches for constructing stochastic models, validation, verification and uncertainty quantification for predictive computational science, and visualization of uncertainty in high-dimensional spaces. Bayesian computation and machine learning techniques are also of interest for example in the context of stochastic multiscale systems, for model selection/classification, and decision making. Reports addressing the dynamic coupling of modern experiments and modeling approaches towards predictive science are particularly encouraged. Applications of uncertainty quantification in all areas of physical and biological sciences are appropriate.