Furthermore, this particular operator proves to be very efficient with regard to the important aspect of computational cost, that is, a reduction in computing time ranging from about 25% up to about 150% compared to the other operators.
This resulted in substantial savings in computing time.
Using these algorithms, it is possible to obtain substantial reduction in computing time and core storage.
The numerical experiments demonstrated 3000x reduction in computing time over standard MTS methods for solving the multiscale model.
Performing the sensitivity analysis using the emulator rather than the consequence model itself leads to significant savings in computing time.
Here we apply the method to one-dimensional electrostatic bounded plasma problems and demonstrate a significant reduction in computing time.
Results show that greedy algorithms that minimize the information entropy perform best, both in computing time and optimality criterion.
The simulation result shows that the proposed algorithm performs well both in computing time and in accuracy.
The proposed technique shows an impressive 98% saving in computing time and 75% saving in storage space for the case study problem.
Because of the reduction of number of unknowns, a significant reduction in computing time can be achieved as compared with that for the analysis of the complete system.
Flexible gridding has the potential of increasing the accuracy in numerical simulation of flow through hydrocarbon reservoirs within limitations in computing time and memory space.
