Abstract

In this article the behavior of a shape function based on the maximum entropy principle (maxent) is analyzed in a meshless collocation method, compared with a traditional fixed weighted least square shape function (FWLS). The maxent shape function used in this work has certain properties that are desired in a meshless collocation method, for example the positivity, the smooth and uniform aspect for different discretizations. Further, in the boundary, the approximation not depends of the shape function of the interior nodes, this property is know as a reduction of the shape function on the boundary. To compare this type of function, it was developed examples that include the solution of eliptical second order equations in 1D and 2D. The numerical results shown a better behavior of the maxent shape function compared with the FWLS, particularly in terms of the convergence and stability of the meshless collocations method that result.

Abstract

At the local level, successful meshless techniques such as the Finite Point Method must have two main characteristics: a suitable geometrical support and a robust numerical approximation built on the former. In this article we develop the second condition and present an alternative procedure to obtain shape functions and their derivatives from a given cloud of points regardless of its geometrical features. This procedure, based on a QR factorization and an iterative adjust of local approximation parameters, allows obtaining a satisfactory minimization problem solution, even in the most difficult cases where usual approaches fail. It is known that high-order meshless constructions need to include a large number of points in the local support zone and this fact turns the approximation more dependent on the size, shape and spatial distribution of the local cloud of points. The proposed procedure also facilitates the construction of high-order approximations on generic geometries reducing their dependence on the geometrical support where they are based. Apart from the alternative solution to the minimization problem, the behaviour of high-order Finite Point approximations and the overall performance of the proposed methodology are shown by means of several numerical tests.