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Neuromorphic computing based on single-electron circuit technology is gaining prominence because of its massively increased computational efficiency and the increasing relevance of computer technology and nanotechnology [Likharev K, Mayr A, Muckra I, Türel Ö. CrossNets: High-performance neuromorphic architectures for CMOL circuits.
All these features lead to increased computational efficiency, and allow applicability of the code to multi-core/processor parallel computing platforms with excellent scalability.
The solution procedure proposed earlier in the literature based upon the above method has been reformulated for increased computational efficiency.
Finally, the genetic algorithm is used to search for the optimum design scenario by evaluating the objective function via proxy models for increased computational efficiency.
Model simulation and multi-parameter optimization were facilitated by the increased computational efficiency achieved from the use of an orthogonal collocation-based reformulated model.
One of the major benefits of the proposed scheme is that the algorithm could be easily implemented in parallel to take advantage of multiple processors for increased computational efficiency.
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To address this challenge, the Kriging surrogate model was proposed to increase computational efficiency.
The local grid adaptation with the high-order scheme is shown to increase computational efficiency.
Adaptive mesh refinement generally aims to increase computational efficiency without compromising the accuracy of the numerical solution.
Algorithm development for DSMC aimed at increasing computational efficiency is discussed with a focus on hybrid particle-continuum methods.
The presented MTP approach increases computational efficiency by taking advantage of approximation methods offered by a novel POMDP solver B3RTDP as well as leveraging value functions at various levels of the MSS as heuristics for value functions at higher levels.
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