Enriching stochastic model updating metrics: An efficient Bayesian approach using Bray-Curtis distance and an adaptive binning algorithm

In practical engineering, experimental data is not fully in line with the true system response due to various uncertain factors, e.g., parameter imprecision, model uncertainty, and measurement errors. presence of mixed sources aleatory epistemic stochastic updating a powerful tool for validation calibration. This paper investigates use Bray-Curtis (B-C) distance proposes Bayesian approach addressing scenario where dataset contains multiple outliers. proposed method, B-C distance-based uncertainty quantification metric employed, that rewards models which discrepancy between observations simulated samples small while penalizing those exhibit large differences. To improve computational efficiency, an adaptive binning algorithm developed embedded into approximate computation framework. The merit this number bins automatically selected according difference data. effectiveness efficiency method verified via two numerical cases engineering case from NASA 2020 UQ challenge. Both static dynamic explicit implicit propagation are considered.