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The possibility of reducing computational cost required for a given level of accuracy by using high-order discretization is examined.
For a given level of accuracy, we are simulating far fewer neurons than might naively be expected.
In this paper, we study what is the optimal sample size to achieve a given level of accuracy while maximizing the total simulation speedup.
This is while for a given level of accuracy in the results, ANSYS requires a markedly larger number of degrees of freedom compared to that needed by the developed method.
Further, several grid convergence studies indicate that the present approach exhibits a higher resolution (and hence requires fewer nodes to reach a given level of accuracy) when ambipolar diffusion is present.
An alternative method, which we applied for this study, requires participants to make numerical interval estimates at a given level of accuracy (McKenzie et al. 2008; Soll and Klayman 2004; Teigen and Jørgensen 2005).
Similar(46)
High-order-accurate methods for viscous flow problems have the potential to reduce the computational effort required for a given level of solution accuracy.
These numerical experiments show that the bandwidth-based approach produces superior convergence rates, and hence requires fewer mesh nodes for a given level of solution accuracy.
Because the system starts with an approximate domain theory, fewer training examples are generally required to attain a given level of classification accuracy compared to a purely empirical learning system.
At the given level of accuracy, results are compatible with Eq. 5 according to which SUR(T) follows straight lines which approximately converge at a common point ((0,bar {V}_{r})).
Robustness to noise informs on the efficiency of a representation for a given task: More efficient representations will typically tolerate more noise for a given level of performance (here, 75% accuracy).
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CEO of Professional Science Editing for Scientists @ prosciediting.com