Uncertainty-Aware Data-Driven Framework for Full-Field Structural Parameter Identification with Measurement Error Compensation

Accurate identification of mechanical properties is crucial for creating simplified equivalent models of complex aircraft structures but is often hindered by material heterogeneity, measurement errors, and multisource uncertainties. This study proposes an uncertainty-aware data-driven framework for full-field identification of spatially varying mechanical properties of aircraft structures, with integrated compensation for random, systematic, and gross measurement errors. Specifically, an enhanced [Formula: see text]-Net model incorporating benchmark-calibrated and pixel-transformed datasets is designed to capture the nonlinear mapping from response fields to parameter distributions, enabling efficient online identification. Interval uncertainty quantification is achieved through statistical modeling of training residuals, providing reliable bounds that reflect uncertainties from data noise and model limitations. To enhance robustness under realistic measurement imperfections, random and gross errors are mitigated via noise-injected and data-missing-augmented training datasets, while systematic errors are corrected using a reduced-order basis approximation optimized through an active-learning-based surrogate modeling scheme. Numerical and experimental validations demonstrate that the proposed method accurately reconstructs full-field parameters, produces reliable uncertainty intervals, and maintains stability under various error conditions, highlighting its potential for structural equivalence modeling and online inverse characterization.