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In general, this is a very hard task for multidimensional problems.
These advantages allow us to compute, for the first time, all physical observables for multidimensional problems in an Eulerian framework.
This paper proposes a novel evidence-theory model for multidimensional problems, with consideration of the dependence among evidence variables.
The method is derived within the finite deformation framework for multidimensional problems by using a total Lagrangian formulation.
But an obvious disadvantage of the BGK scheme is the low computational efficiency, in particular for multidimensional problems.
In contrast, previous methods used ill-advised dimensional splitting for multidimensional problems and suffered from great complexity when used in conjunction with multilevel time integrators.
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More specifically, a joint frame of discernment (FD) is created for multidimensional problem through an ellipsoidal model, where the correlativity between the parameters is well reflected by the shape of the ellipsoid.
Reference [8] used PSO with chaotic opposition based population initialization, called the CSPSO, to enhance performance of basic PSO for multidimensional problem.
It allows for an accurate and efficient treatment of multidimensional problems with variable coefficients, nonlinearities, and general boundary conditions.
They are particularly useful for complex multidimensional problems.
A numerical method was developed to solve the population balance equation for transient multidimensional problems including particle particle interactions.
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