Sentence examples for surrogate framework from inspiring English sources

Elastic modulus prediction of short fibre composites with polyester and vinylester is presented using the surrogate framework supported by multiple spatially distributed surrogate models of different types.

In the surrogate framework, several surrogate models are validated and the PWS (Predicted residual sum of square-based Weighted average Surrogate) model is chosen for our further investigation.

An optimal surrogate model selection framework, called Concurrent Surrogate Model Selection, or COSMOS, was utilized to identify the surrogate models best suited to approximate each objective function.

Based on the improved RBF surrogate model, a framework and detailed procedure for the SAO algorithm is presented, and the performance of the proposed SAO algorithm is tested, with obtained results showing that the proposed SAO algorithm reduces the calling times of the original model and improves the optimization efficiency remarkably.

A stochastic optimization framework combining stochastic surrogate model representation and optimization algorithm is proposed.

An adaptive surrogate modeling framework is developed to locate the lowest reliability value within a multi-dimensional interval.

Optimization is performed using a derivative-free algorithm called the surrogate management framework, with constraints enforced using a filter method.

Our method extends the previously developed surrogate management framework (SMF) to allow for uncertainties in both simulation parameters and design variables.

The optimization method employed is a tailored version of the surrogate management framework, a method that was developed previously for expensive functions with little or no gradient information.

In this study, a surrogate modeling framework has been introduced to map the effect of design-related parameters on the performance of a lithium-ion cell.

The oil reservoir, being treated as a multi-input, multi-output (MIMO) system in terms of injection inputs and production outputs, is identified within a robust surrogate modeling framework using a frequency-based system identification approach.