Sentence examples for multiple surrogate models from inspiring English sources

A design demonstration of USBSD-HX reveals that the simultaneous use of multiple surrogate models gives better results.

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.

The key property of our MMFIS estimator is that it can leverage multiple surrogate models for the construction of the biasing distribution, instead of a single surrogate model alone.

Although there are many mature techniques in machine learning [132] and data mining [133] that can help learn preferences of DMs, little attention has been paid to this research topic with a few exceptions [134], where preferences of DMs are learned by training a single or multiple surrogate models [135] using a semi-supervised learning algorithm.

The design parameters, platinum loading, platinum mass ratio, electrolyte volume fraction, thickness of catalyst layer and agglomerate radius, are optimised by a multiple surrogate model and their sensitivities are analysed by a Monte Carlo method based approach.

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.

Two well known crashworthiness indicators are considered as contrasting objective functions: the Specific Energy Absorption (SEA) and the Load Ratio (LR), whose responses are approximated by multiple types of surrogate models due to their computational cost and their noise levels.

In order to alleviate the computational cost, Kriging modeling technique is adopted to generate the surrogate models of structural responses under multiple load cases.

Latin Hypercube Sampling is used to sample the design space, while Radial Basis Function methodology is used to develop surrogate models for the selected crash responses at multiple sites as well as the first three fundamental natural frequencies.

Simple models that link IBIs directly to single or multiple surrogate stressors such as percent imperviousness are inadequate because they may not represent a true cause effect proximate relationship.

While this is a successful strategy to improve optimization efficiency, challenges arise when constructing surrogate models in higher dimensional function space, where the trade space between multiple conflicting objectives is increasingly complex.