An efficient model updating method based on variational Bayesian inference with Wasserstein distance metric

To obtain the posterior distribution of parameters for expensive numerical models with complex dynamic responses that require substantial computational resources, a model updating method utilizing the Wasserstein distance as uncertainty quantification (UQ) metric and variational Bayesian Monte Carlo (VBMC) for parameter posterior identification is proposed. Combined with MATLAB, Nastran and Adams software for secondary development, the traditional finite element model updating technology is extended to the rigid-flexible coupling model. The application results in the composite plate, satellite rigid-flexible model, and solar wing deployment model demonstrate that the updated models exhibit high precision. Compared to existing UQ methods, the Wasserstein distance can more robustly measure the discrepancy between two samples and significantly reduce model structure and parameter uncertainty. The VBMC method converges to the true parameter values after a few number of iterations, and the updating efficiency is markedly higher than that of the random-sampling-based Markov chain Monte Carlo (MCMC) method.