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In the future work, we will use computer grid technology to improve computational efficiency.
To improve computational efficiency of large scale three-dimensional computations, the DG-FTLE algorithm is adapted to parallel architectures.
We present three variations of HPB, each with a different algorithm for computing hyperbolic areas, in order to improve computational efficiency and localization granularity.
In order to improve computational efficiency, this paper explores Design of Computer Experiments and Neural Networks for representation of structural behavior.
Direct, specific fitting of ellipses and hyperbolae is achieved by imposing a quadratic constraint on the conic coefficients, whereby an improved partitioning of the design matrix is devised so as to improve computational efficiency and numerical stability by eliminating redundant aspects of the fitting procedure.
To improve computational efficiency in 3D case, we adopt graphic processing unit (GPU) with Compute Unified Device Architecture (CUDA) instead of traditional central processing unit (CPU) architecture.
To improve computational efficiency, we used core SNP set in the follow-up analyses.
To improve computational efficiency, the inversion was carried out in the frequency domain.
Finally, an adaptive parallel strategy is proposed to further improve computational efficiency.
The second model, however, requires further work to improve computational efficiency.
In order to improve computational efficiency, we use the algorithm framework of the paper [14] to deal with our model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com