Distribution-free stochastic model updating of dynamic systems with parameter dependencies

This work proposes a distribution-free stochastic model updating framework to calibrate the joint probabilistic distribution of multivariate correlated parameters. In this framework, marginal distributions are defined as staircase density functions and correlation structure is described by Gaussian copula function. The first four moments coefficients updated an approximate Bayesian computation, in which Bhattacharyya distance-based metric proposed define likelihood that capable capturing discrepancy between outputs observations. feasibility demonstrated on two illustrative examples followed engineering application nonlinear dynamic system using observed time signals. results demonstrate capability procedure very challenging condition where prior knowledge about parameters extremely limited (i.e., no information families available).