Sentence examples for achieving computational efficiency from inspiring English sources
In this study, a surrogate based approach using General High Dimensional Model Representations (GHDMR) is employed for achieving computational efficiency in quantifying uncertainty.
Although achieving computational efficiency relative to commonly used second order implicit schemes has been the motivating factor, this study focuses on temporal order analysis of the high order schemes on the collocated grid.
The main advantages of MC-SSL over SSL are (a) support for end-to-end security in the presence of partially trusted proxies, and (b) selective data protection for achieving computational efficiency important to resource-constrained clients and heavily loaded servers.
To achieve computational efficiency on parallel computers, the linear system was treated by an inverse matrix method (a sparse matrix vector multiplication).
A mesoscale description is employed for representing the heterogeneous behaviour of masonry units, mortar joints and brick-mortar interfaces, whereas a domain partitioning approach allowing for parallel computation is used to achieve computational efficiency.
Instead, the algorithm computes a k-mer based distance between pairs of input sequences and achieves computational efficiency.
However, most existing algorithms cannot achieve computational efficiency and accuracy simultaneously.
In the same spirit of exploiting structure to achieve computational efficiency, an algorithm for the numerical solution of a special class of frequency-dependent LMIs is presented.
The SVR based uncertainty quantification (UQ) algorithm in conjunction with Latin hypercube sampling is developed to achieve computational efficiency.
Therefore, we also applied the model order reduction (MOR) scheme to this coupled multiphysical problem in order to achieve computational efficiency.
In this research a method is developed to achieve computational efficiency by implementing the adaptively shifted integration-Gauss technique in conjunction with a core neural network metamodel.
