Statistical inverse identification for nonlinear train dynamics using a surrogate model in a Bayesian framework

This paper presents a Bayesian calibration method for a simulation-based model with stochastic functional input and output. The originality of the method lies in an adaptation involving the representation of the likelihood function by a Gaussian process surrogate model, to cope with the high computational cost of the simulation, while avoiding the surrogate modeling of the functional output. The adaptation focuses on taking into account the uncertainty introduced by the use of a surrogate model when estimating the parameters posterior probability distribution by MCMC. To this end, trajectories of the random surrogate model of the likelihood function are drawn and injected in the MCMC algorithm. An application on a train suspension monitoring case is presented. •Functional outputs of expensive computer codes is used for Bayesian calibration.•A new approach based on a surrogate model of the likelihood function is used.•A Gaussian process models allows for including the surrogate model uncertainty.•An application is done for high-speed train nonlinear dynamics with measurements.