A new uncertainty importance measure

Borgonovo, Emanuele

doi:10.1016/j.ress.2006.04.015

Cited by 816 publications

(583 citation statements)

References 36 publications

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“…As explained in [34], to compute the sensitivity function S i (t) (10) of parameter p i , it is necessary to reorder the expansion (17) in order to separate the different contributions -single and collective -of each parameter, as (2). To do so, I k1,...,ks must define the set of multi-indices j such that :…”

Section: Pc-based Sensitivity Functionsmentioning

confidence: 99%

“…The computation of the sensivity functions of the parameters for the dynamic model (2), requires the following steps: a) PC decomposition 1. Polynomial type Choose the polynomial type associated to the parameter distribution, see Table 1.…”

Section: Summary Of the Proposed Approachmentioning

confidence: 99%

“…To prepare and plan the experiments, it is necessary to distinguish the parameters with an insignificant influence on the response uncertainty, so as to set them at their nominal value in their interval of variation, thanks to the sensitivity analysis. Numerous studies have focused on the sensitivity analysis for static nonlinear models, for example, [1,2,3,4,5,6,7]. The approaches may be local or global.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity study of dynamic systems using polynomial chaos

Sandoval

Anstett-Collin

Basset

2012

Reliability Engineering & System Safety

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Global sensitivity has mainly been analyzed in static models, though most physical systems can be described by differential equations. Very few approaches have been proposed for the sensitivity of dynamic models and the only ones are local. Nevertheless, it would be of great interest to consider the entire uncertainty range of parameters since they can vary within large intervals depending on their meaning. Other advantage of global analysis is that the sensitivity indices of a given parameter are evaluated while all the other parameters can be varied. In this way, the relative variability of each parameter is taken into account, revealing any possible interactions. This paper presents the global sensitivity analysis for dynamic models with an original approach based on the polynomial chaos (PC) expansion of the output. The evaluation of the PC expansion of the output is less expensive compared to direct simulations. Moreover, at each time instant, the coefficients of the PC decomposition convey the parameter sensitivity and then a sensitivity function can be obtained. The PC coefficients are determined using non-intrusive methods. The proposed approach is illustrated with some well-known dynamic systems.

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Section: Pc-based Sensitivity Functionsmentioning

confidence: 99%

Section: Summary Of the Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensitivity study of dynamic systems using polynomial chaos

Sandoval

Anstett-Collin

Basset

2012

Reliability Engineering & System Safety

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“…Among these, the invariant method Delta (Borgonovo 2007) and Kolmogorov-Smirnov distance between cumulative distribution functions (Baucells and Borgonovo, 2013) have to be cited as viable solutions.…”

Section: Introductionmentioning

confidence: 99%

“…As shown in (Borgonovo et al (2014)), in fact, variance-based and distribution-based sensitivity measures rest on a common rationale that allows them to be estimated from the same design of experiments. We can, then, employ an ensemble of three SA indicators: first-order variance-based sensitivity measure (i.e., the Pearson's correlation ratio), the Delta method (Borgonovo 2007) and a new sensitivity measure based on the Kolmogorov-Smirnov distance between cumulative distribution functions (Baucells and Borgonovo, 2013). We use these sensitivity measures for ranking the input variables most affecting the output uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Invariant methods for an ensemble-based sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant

Maio

Nicola

Borgonovo

et al. 2016

Reliability Engineering & System Safety

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Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models

Rakovec¹,

et al. 2014

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[1] This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based ''local'' methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative ''bucket-style'' hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km 2 ) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.

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A new uncertainty importance measure

Cited by 816 publications

References 36 publications

Sensitivity study of dynamic systems using polynomial chaos

Sensitivity study of dynamic systems using polynomial chaos

Invariant methods for an ensemble-based sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant

Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models

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