Physics-based pruning neural network for global sensitivity analysis

Zhiwei Bai and Shufang Song

Reliability Engineering and System Safety, 2025, vol. 258, issue C

Abstract: Global sensitivity analysis (GSA) is essential to quantify the variation of model response sourced from the uncertainty of input variables over the entire design space. To address challenges in high-dimensional complex problems with dependent variables, a novel physics-based pruning neural network (PbPNN) approach is proposed. The PbPNN innovatively performs network pruning based on the properties of unconditional and conditional variances. Through the mask matrix of specific settings, a pruning neural network with 3-dimensional outputs (an unconditional and two conditional responses) is constructed. The PbPNN method not only simultaneously calculates the unconditional and conditional variances but also effectively identifies the contributions from variable dependencies and interactions. Furthermore, the PbPNN method remains unaffected by the dimensionality of the problem, making it well-suited for high-dimensional complex problems. The effectiveness and accuracy of the proposed method are demonstrated through three numerical examples, where the PbPNN outperformed traditional methods in both sensitivity quantification and computational efficiency. Two engineering examples further validate the method's potential, proving the value of combining machine learning with the properties of unconditional and conditional variances in GSA.

Keywords: Global sensitivity analysis; Neural networks; Physics-based pruning strategy; System safety; Surrogate models (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832025001280
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:258:y:2025:i:c:s0951832025001280

DOI: 10.1016/j.ress.2025.110925

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().