Global Sensitivity Analysis Based on Entropy: From Differential Entropy to Alternative Measures

Kala, Zdeněk

doi:10.3390/e23060778

Cited by 14 publications

(4 citation statements)

References 75 publications

(114 reference statements)

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“…For instance, entropy-based methods and methods that consider the entire probability density function are favorable when there is a strong skewness within the output distribution or when the output is multi-modal. 65,66 There was, however, no strong skewness found in our outputs of interest, so there is a strong indication that the variance is a good proxy for the uncertainty of our model. Nonetheless, one should consider this before applying SSA to other applications.…”

Section: Verification Of Ssa Approachmentioning

confidence: 76%

“…However, other studies have indicated that other methods might be preferable if the variance is not a good measure. For instance, entropy‐based methods and methods that consider the entire probability density function are favorable when there is a strong skewness within the output distribution or when the output is multi‐modal 65,66 . There was, however, no strong skewness found in our outputs of interest, so there is a strong indication that the variance is a good proxy for the uncertainty of our model.…”

Section: Discussionmentioning

confidence: 99%

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Efficient sensitivity analysis for biomechanical models with correlated inputs

Hilhorst,

Quicken,

van de Vosse

et al. 2023

Numer Methods Biomed Eng

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In most variance‐based sensitivity analysis (SA) approaches applied to biomechanical models, statistical independence of the model input is assumed. However, often the model inputs are correlated. This might alter the interpretation of the SA results, which may severely impact the guidance provided during model development and personalization. Potential reasons for the infrequent usage of SA techniques that account for input correlation are the associated high computational costs, especially for models with many parameters, and the fact that the input correlation structure is often unknown. The aim of this study was to propose an efficient correlated global sensitivity analysis method by applying a surrogate model‐based approach. Furthermore, this article demonstrates how correlated SA should be interpreted and how the applied method can guide the modeler during model development and personalization, even when the correlation structure is not entirely known beforehand. The proposed methodology was applied to a typical example of a pulse wave propagation model and resulted in accurate SA results that could be obtained at a theoretically 27,000× lower computational cost compared to the correlated SA approach without employing a surrogate model. Furthermore, our results demonstrate that input correlations can significantly affect SA results, which emphasizes the need to thoroughly investigate the effect of input correlations during model development. We conclude that our proposed surrogate‐based SA approach allows modelers to efficiently perform correlated SA to complex biomechanical models and allows modelers to focus on input prioritization, input fixing and model reduction, or assessing the dependency structure between parameters.

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Section: Verification Of Ssa Approachmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Efficient sensitivity analysis for biomechanical models with correlated inputs

Hilhorst,

Quicken,

van de Vosse

et al. 2023

Numer Methods Biomed Eng

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show abstract

“…Entropy-based SA also belongs to the category of density-based SA, where uncertainty is characterized by examining the entire distribution of model outputs, not just its variance. The use of entropy instead of variance is usually justified by the need to analyze the output random variable with heavy-tail or outliers [23]. However, density-based indices are more difficult to implement than variance-based ones, owing to the fact that their computation demands the understanding of a large number of conditional PDFs [21].…”

Section: Distribution-based Approachmentioning

confidence: 99%

Real-Time Event-Driven Learning in Highly Volatile Systems: A Case for Embedded Machine Learning for SCADA Systems

Goncalves

Sousa²,

Mendes

et al. 2022

IEEE Access

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Extracting key system parameters and their impact on state transition is a necessity for knowledge and data engineering. In Decision Support Systems, the quest for yet more efficient and faster methods of sensitivity analysis (SA) and feature extraction in complex and volatile systems persists. A new improved event tracking methodology, the fastTracker, for real-time SA in large scale complex systems is proposed in this paper. The main feature of fastTracker is its high-frequency analytics using meager computational cost. It is suitable for data processing and prioritization in embedded systems, Internet of Things (IoT), distributed computing (e.g. lEdge computing) applications. The presented algorithm's underpinning rationale is event driven; its objective is to correctly and succinctly quantify the sensitivity of observable changes in the system (output) with respect to the input variables. To demonstrate the performance of the proposed fastTracker methodology, fastTracker was deployed in the Supervisory control and data acquisition (SCADA) system from real cement industry. fastTracker has been verified by system experts in real industrial application. Its performance was compared with other real-time event-based SA techniques. The comparison revealed savings of 98.8% in processing time per sensitivity index and 20% in memory usage when compared with EventTracker, its closest rival. The proposed methodology is more accurate and 80.9% faster than an entropy-based method. Its application is recommended for reinforced learning and/or formulating system key performance indicators from raw data.

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“…In some of our previous works [9,35,36], the problem of the sensitivity analysis of output model characteristics to the distributions of the input characteristics was considered. The papers of Z. Kala presented a general approach to the problems of sensitivity analysis [37,38].…”

Section: Introductionmentioning

confidence: 99%

Renewable k-Out-of-n System with the Component-Wise Strategy of Preventive System Maintenance

2023

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At the SMARTY-22 conference, a review of the regenerative methods development was presented, including its application to the study of a non-renewable k-out-of-n system. This paper develops the previous study for the renewable k-out-of-n system, including an investigation different preventive maintenance strategies based on the system state observation. We also include the review of Smith’s regeneration idea development. Some new results are presented that form the basis for an algorithm for comparing preventing maintenance strategies with respect to the maximization of the availability factor. A numerical study was conducted for the 4-out-of-6 and 4-out-of-8 models. The study demonstrates the sensitivity of decision making to the shape of the repair time distribution.

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Global Sensitivity Analysis Based on Entropy: From Differential Entropy to Alternative Measures

Cited by 14 publications

References 75 publications

Efficient sensitivity analysis for biomechanical models with correlated inputs

Efficient sensitivity analysis for biomechanical models with correlated inputs

Real-Time Event-Driven Learning in Highly Volatile Systems: A Case for Embedded Machine Learning for SCADA Systems

Renewable k-Out-of-n System with the Component-Wise Strategy of Preventive System Maintenance

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