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The main advantage of DSD is the reduction in the computational cost of efficient detection schemes that, for its high computational complexity, are not feasible for implementation when the signals received from all active users are coupled.
The numerical results clearly indicate that the MLMC-FVM framework can approximate statistics of quantities of interest such as run-up heights, quite accurately and with reasonable computational cost under efficient implementation on GPUs.
As the classification task is often combined with other high computational cost functions, efficient performance of the main modules is fundamental requirements to achieve hard real-time speed for the whole system.
Additionally, we provide an ROI-based variation of the method as well for a reduced computational cost and an efficient phase error estimation.
This approach, though, involves an unavoidable estimation error and significant computational cost, features which make efficient design optimization challenging.
Fast sensitivity analysis and very efficient computational cost are the benefit of the derived analytical solutions with respect to previous numerical solutions.
This allows to tremendously reduce the computational cost and to perform efficient numerical simulations of kinetic equations up to the six-dimensional case without parallelization.
Another direction for reducing computational cost is by an efficient exploration of discrete energy landscapes, which was developed in a recent work [ 51] by introducing a sampling method that allows for a fast yet accurate estimation of the transition probabilities between macrostates when coarse graining of the state space is used [ 4, 16, 50, 52, 53].
Our adjoint optimization framework has the following salient features: (1) The adjoint computation is efficient, with a computational cost that is similar to that of the primal flow solver and scales up to 10 million cells and 1024 CPU cores.
To reduce the high computational cost of dense kernel descriptors, efficient match kernel (EMK) is introduced which maps local features to a low-dimensional feature space and average the resulting vectors to form a set-level feature [67].
