Variable-fidelity model selection for stochastic simulation

This paper presents a model selection methodology for maximizing the accuracy in the predicted distribution of a stochastic output of interest subject to an available computational budget. Model choices of different resolutions/fidelities such as coarse vs. fine mesh and linear vs. nonlinear material model are considered. The proposed approach makes use of efficient simulation techniques and mathematical surrogate models to develop a model selection framework. The model decision is made by considering the expected (or estimated) discrepancy between model prediction and the best available information about the quantity of interest, as well as the simulation effort required for the particular model choice. The form of the best available information may be the result of a maximum fidelity simulation, a physical experiment, or expert opinion. Several different situations corresponding to the type and amount of data are considered for a Monte Carlo simulation over the input space. The proposed methods are illustrated for a crack growth simulation problem in which model choices must be made for each cycle or cycle block even within one input sample.