Statistical emulation of large linear dynamic models

Abstract

The paper describes a new methodology for the emulation of high order, dynamic simulation models. This exploits the technique of dominant mode analysis to identify a reduced-order, linear transfer function model that closely reproduces the linearized dynamic behaviour of the large model. Based on a set of such reduced-order models, identified over a specified region of the large model's parameter space, non-parametric regression, tensor product cubic spline smoothing or Gaussian process emulation are utilized to construct a computationally efficient, low order, dynamic emulation (or meta) model that can replace the large model in applications such as sensitivity analysis, forecasting or control system design. Two modes of emulation are possible, one of which allows for novel `stand-alone' operation that replicates the dynamic behaviour of the large simulation model over any time horizon and any sequence of the forcing inputs. Two examples demonstrate the practical utility of the proposed technique and supplementary materials, available on-line and including Matlab code, provide a background to the methods of transfer function model identication and estimation used in the paper.