A decision-theoretic method for surrogate model selection.

Abstract

The use of surrogate models to approximate computationally expensive simulation models, e.g., large comprehensive finite element models, is widespread. Applications include surrogate models for design, sensitivity analysis, and/or uncertainty quantification. Typically, a surrogate model is defined by a postulated functional form; values for the surrogate model parameters are estimated using results from a limited number of solutions to the comprehensive model. In general, there may be multiple surrogate models, each defined by possibly a different functional form, consistent with the limited data from the comprehensive model. We refer to each as a candidate surrogate model. Methods are developed and applied to select the optimal surrogate model from the collection of candidate surrogate models. The classical approach is to select the surrogate model that best fits the data provided by the comprehensive model; this technique is independent of the model use and, therefore, may be inappropriate for some applications. The proposed approach applies techniques from decision theory, where postulated utility functions are used to quantify the model use. Two applications are presented to illustrate the methods. These include surrogate model selection for the purpose of: (1) estimating the minimum of a deterministic function, and (2) the design under uncertainty of a physical system.

Model selection for a class of stochastic processes or random fields with bounded range

“…This approach has proved successful when applied to high risk systems with a fair understanding of the consequences of unsatisfactory system behavior. Example applications include the shock response mitigation of a flexible structure during penetration of a hard target [23], the detection and monitoring of vehicles in the vicinity of critical national assets [24], and aerodynamics models for turbulent pressure fluctuations during atmospheric re-entry of a spacecraft [25].…”