Sentence examples for an efficient surrogate from inspiring English sources

The probabilistic Bayesian identification is carried out based on an efficient surrogate model through the use of Markov Chain Monte Carlo sampling methods.

Since Monte-Carlo simulations are computationally very expensive, we discuss here an efficient surrogate modeling scheme based on the Polynomial Chaos Expansion method to compute the field quality in the presence of tolerances and misalignments and subsequently to perform the sensitivity analysis at zero additional computational cost.

Therefore, in an effort to simplify this procedure, several attempts have been made to replace this analysis with an efficient surrogate marker.

This paper describes an accurate and computationally efficient surrogate method, known as the polynomial dimensional decomposition (PDD) method, for estimating a general class of density-based f-sensitivity indices.

This study proposes the use of non-intrusive polynomial chaos expansions (PCEs) as a means to significantly accelerate UP analysis in SWI numerical modeling studies and shows that despite the highly non-linear and non-smooth input/output relationship that exists in SWI models, non-intrusive PCEs provide a reliable and yet computationally efficient surrogate of the original numerical model.

An adjoint-response surface method is developed to provide efficient surrogate model in a parametrized design space for aerodynamic optimization of turbomachinery blades.

The paper presents a computationally efficient surrogate-based optimization algorithm for the inverse design of transonic airfoils.

Were the neural networks to be useful to such a mapping, they could in principle be employed for several purposes, such as soil identification using instrumental data, design of early warning systems and estimation of probabilistic spectra via Monte Carlo simulation, in which the ANN act as an efficient solver surrogate.

Latin hypercube sampling is utilised to train the efficient surrogate models, which are employed as a medium of uncertainty propagation.

In the latter case, our framework suggests novel, efficient surrogate objectives and algorithms that outperform classical techniques.

However, the heuristic nature of estimation of parameters associated with ANNs has prevented their evolution into efficient surrogate models.