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Abstract
This paper introduces an interval-based non-probabilistic sensitivity analysis method, named subinterval sensitivity. A powerful, reliable and rigorous sensitivity analysis method, which is best suited to quantify the importance of inputs purely with respect to their mathematical model. The method has only recently and partially appeared in the literature, while its scalability to high-dimensional models is claimed here for the first time. We apply subinterval sensitivity to quantify and rank the importance of the parameters of a trained neural network model while drawing comparisons with the established Sobol' sensitivity analysis method. Sensitivities on the parameters of a trained neural network can shed light on overparametrization and explainability of the neural network surrogate model.
Original language | English |
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Title of host publication | Proc. of the 35th European Safety and Reliability & 33rd Society for Risk Analysis Europe Conference |
Place of Publication | Singapore |
Number of pages | 8 |
Publication status | Accepted/In press - 15 Feb 2025 |
Event | ESREL & SRA-E 2025: ESREL SRA-E 2025 - University of Stavanger, Stavanger, Norway Duration: 15 Jun 2025 → 19 Jun 2025 https://esrel2025.com/ |
Conference
Conference | ESREL & SRA-E 2025 |
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Abbreviated title | ESREL SRA-E 2025 |
Country/Territory | Norway |
City | Stavanger |
Period | 15/06/25 → 19/06/25 |
Internet address |
Funding
This research is funded by the University of Strathclyde’s StrathDRUMS centre for doctoral training.
Keywords
- sensitivity analysis
- interval computation
- subinterval reconstitution
- high-dimensional models
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SAMO 2025
De Angelis, M. (Participant) & Ochnio, D. (Speaker)
23 Apr 2025Activity: Participating in or organising an event types › Participation in conference