What question did this study set out to answer?

This research aims to improve finite element model updating for complex aircraft structures by integrating advanced methodologies.

July 1, 2026

An adaptive two-stage finite element model updating method fusing Bayesian neural networks and trust region resampling

Key Points

This research aims to improve finite element model updating for complex aircraft structures by integrating advanced methodologies.
Developed an adaptive two-stage framework combining Bayesian regularisation neural networks with trust-region resampling.
Constructed a coarse surrogate model using Latin hypercube sampling and Bayesian inference.
Applied the non-dominated sorting genetic algorithm II to identify promising candidate solutions followed by focused high-fidelity resampling.
Average modal frequency error reduced from 4.56% to 2.39%.
The method alleviated the smoothing effect found in traditional global surrogate models.
Achieved better parameter convergence and reliable updates for material and beam parameters.

Abstract

Abstract This paper addresses the challenge of obtaining accurate finite element model updating (FEMU) results for complex flexible aircraft structures without relying on prohibitively large numbers of direct finite element evaluations. An adaptive two-stage framework is proposed by integrating Bayesian regularisation neural networks (BR-ANN) with trust-region resampling. In the global stage, a coarse-grained surrogate model is constructed via Latin hypercube sampling (LHS) and Bayesian inference, enabling the non-dominated sorting genetic algorithm II (NSGA-II) to identify the Pareto-front region containing promising candidate solutions. Subsequently, a trust-region strategy performs high-fidelity resampling within the local neighbourhood of the knee-point solution, establishing a refined surrogate model that reduces local surrogate bias and improves parameter convergence. Validation using the GARTEUR SM-AG19 benchmark aircraft demonstrates that the proposed method effectively mitigates the smoothing effect inherent in conventional global surrogate models while maintaining computational efficiency. The average modal frequency error was reduced from 4.56% to 2.39%, and the method supports physically interpretable updating of material and equivalent beam parameters, providing a reliable baseline for subsequent aeroelastic predictions.

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