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Deep learning-driven interval uncertainty propagation for aeronautical structures
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Deep learning-driven interval uncertainty propagation for aeronautical structures
Interval Uncertainty Propagation(IUP)holds significant importance in quantifying uncertainties in structural outputs when confronted with interval input parameters.In the aviation field,the precise determination of probability models for input parameters of aeronautical struc-tures entails substantial costs in both time and finances.As an alternative,the use of interval vari-ables to describe input parameter uncertainty becomes a pragmatic approach.The complex task of solving the IUP for aeronautical structures,particularly in scenarios marked by pronounced non-linearity and multiple outputs,necessitates innovative methodologies.This study introduces an effi-cient deep learning-driven approach to address the challenges associated with IUP.The proposed approach combines the Deep Neural Network(DNN)with intelligent optimization algorithms for dealing with the IUP in aeronautical structures.An inventive extremal value-oriented weighting technique is presented,assigning varying weights to different training samples within the loss func-tion,thereby enhancing the computational accuracy of the DNN in predicting extremal values of structural outputs.Moreover,an adaptive framework is established to strategically balance the glo-bal exploration and local exploitation capabilities of the DNN,resulting in a predictive model that is both robust and accurate.To illustrate the effectiveness of the developed approach,various appli-cations are explored,including a high-dimensional numerical example and two aeronautical struc-tures.The obtained results highlight the high computational accuracy and efficiency achieved by the proposed approach,showcasing its potential for addressing complex IUP challenges in aeronautical engineering.
Institute for Risk and Reliability,Leibniz Universität Hannover,Hannover 30167,Germany
Department of Civil and Environmental Engineering,University of Liverpool,Liverpool L69 3BX,United Kingdom
International Joint Research Center for Resilient Infrastructure & International Joint Research Center for Engineering Reliability and Stochastic Mechanics,Tongji University,Shanghai 200092,China
Uncertainty propagation Interval variable Deep learning Optimization algorithm Aeronautical structure
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