Learning of model discrepancy for structural dynamics applications using Bayesian history matching

Abstract

Calibration of computer models for structural dynamics is often an important task in creating valid predictions that match observational data. However, calibration alone will lead to biased estimates of system parameters when a mechanism for model discrepancy is not included. The definition of model discrepancy is the mismatch between observational data and the model when the 'true' parameters are known. This will occur due to the absence and/or simplification of certain physics in the computer model. Bayesian History Matching (BHM) is a 'likelihood-free' method for obtaining calibrated outputs whilst accounting for model discrepancies, typically via an additional variance term. The approach assesses the input space, using an emulator of the complex computer model, and identifies parameter sets that could have plausibly generated the target outputs. In this paper a more informative methodology is outlined where the functional form of the model discrepancy is inferred, improving predictive performance. The algorithm is applied to a case study for a representative five storey building structure with the objective of calibrating outputs of a finite element (FE) model. The results are discussed with appropriate validation metrics that consider the complete distribution.