Statistical emulation of large linear dynamic models

Date

2011

Authors

Young, Peter C
Ratto, Marco

Journal Title

Journal ISSN

Volume Title

Publisher

American Statistical Association

Abstract

The article 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 behavior 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, nonparametric regression, tensor product cubic spline smoothing, or Gaussian process emulation are used 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 behavior 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 online and including Matlab code, provide a background to the methods of transfer function model identification and estimation used in the article.

Description

Keywords

Keywords: Dominant mode; Gaussian Processes; Non-parametric regression; Tensor product cubic spline smoothing; Transfer function analysis; Computer simulation; Gaussian distribution; Gaussian noise (electronic); MATLAB; Regression analysis; Sensitivity analysis; Sp Dominant mode analysis; Gaussian process emulation; Nonparametric regression; Sensitivity analysis; Tensor product cubic spline smoothing; Transfer function analysis

Citation

Source

Technometrics

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

10.1198/TECH.2010.07151

Restricted until

2037-12-31