TY - GEN
T1 - Interval-Based Global Sensitivity Analysis for Epistemic Uncertainty
AU - Miralles-Dolz, Enrique
AU - Gray, Ander
AU - de Angelis, Marco
AU - Patelli, Edoardo
N1 - Publisher Copyright:
© 2022 ESREL2022 Organizers. Published by Research Publishing, Singapore.
PY - 2022
Y1 - 2022
N2 - The objective of sensitivity analysis is to understand how the input uncertainty of a mathematical model contributes to its output uncertainty. In the context of a digital twin, sensitivity analysis is of paramount importance for the automatic verification and validation of physical models, and the identification of parameters which require more empirical investment. Yet, sensitivity analysis often requires making assumptions, e.g., about the probability distribution functions of the input factors, about the model itself, or relies on surrogate models for the evaluation of the sensitivity that also introduce more assumptions. We present a non-probabilistic sensitivity analysis method which requires no assumptions about the input probability distributions: the uncertainty in the input is expressed in the form of intervals, and employs the width of the output interval as the only measure. We use the Ishigami function as test case to show the performance of the proposed method, and compare it with Sobol’ indices.
AB - The objective of sensitivity analysis is to understand how the input uncertainty of a mathematical model contributes to its output uncertainty. In the context of a digital twin, sensitivity analysis is of paramount importance for the automatic verification and validation of physical models, and the identification of parameters which require more empirical investment. Yet, sensitivity analysis often requires making assumptions, e.g., about the probability distribution functions of the input factors, about the model itself, or relies on surrogate models for the evaluation of the sensitivity that also introduce more assumptions. We present a non-probabilistic sensitivity analysis method which requires no assumptions about the input probability distributions: the uncertainty in the input is expressed in the form of intervals, and employs the width of the output interval as the only measure. We use the Ishigami function as test case to show the performance of the proposed method, and compare it with Sobol’ indices.
KW - digital twin
KW - interval arithmetic
KW - sensitivity analysis
KW - sobol indices
KW - uncertainty quantification
UR - https://www.scopus.com/pages/publications/85165940123
UR - https://www.scopus.com/inward/citedby.url?scp=85165940123&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-5183-4_S14-04-180-cd
DO - 10.3850/978-981-18-5183-4_S14-04-180-cd
M3 - Conference contribution
AN - SCOPUS:85165940123
SN - 9789811851834
T3 - Proceedings of the 32nd European Safety and Reliability Conference, ESREL 2022 - Understanding and Managing Risk and Reliability for a Sustainable Future
SP - 2545
EP - 2552
BT - Proceedings of the 32nd European Safety and Reliability Conference, ESREL 2022 - Understanding and Managing Risk and Reliability for a Sustainable Future
A2 - Leva, Maria Chiara
A2 - Patelli, Edoardo
A2 - Podofillini, Luca
A2 - Wilson, Simon
PB - Research Publishing
T2 - 32nd European Safety and Reliability Conference, ESREL 2022
Y2 - 28 August 2022 through 1 September 2022
ER -