2008
DOI: 10.1016/j.cma.2008.02.035
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The piecewise polynomial partition of unity functions for the generalized finite element methods

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Cited by 42 publications
(47 citation statements)
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“…Fortran code for r (x, y) can be found in our previous paper [22]. In [20], we prove the decomposition of suppψ δ k into subsets on which the convolution PU function is a polynomial.…”
Section: Definition 31mentioning
confidence: 99%
See 1 more Smart Citation
“…Fortran code for r (x, y) can be found in our previous paper [22]. In [20], we prove the decomposition of suppψ δ k into subsets on which the convolution PU function is a polynomial.…”
Section: Definition 31mentioning
confidence: 99%
“…5), we use the conformal mapping T α , α = 1/2. Thus, the components J * i j of the inverse matrix and the determinant |J (T s )| are those in (19) and (20).…”
Section: Numerical Examplementioning
confidence: 99%
“…The technique we used is the so-called partition of unity finite element method (PUFEM) [15,20]. We refer the readers to [16][17][18][19] and references therein for recent developments and applications of the PUFEM. Although pointed out in the fundamental paper of Melenk and Babuška [20], the ability of the PUFEM to construct finite element spaces of high regularity has not been fully…”
Section: Introductionmentioning
confidence: 99%
“…Over the past two decades, the concept of partition of unity (PU) approximations has been established and developed into different types of PU-based methods for solid mechanics including the Partition of Unity method [1][2], hp clouds [3], the generalized finite element method [4], the octree partition of unity method (OctPUM) [5] and others [6][7][8][9]. PUFEMs have attracted much interest from researchers in computational solid mechanics as they offer several advantages over the conventional finite element method (FEM), such as a free choice of local approximation functions, which allows flexibility for modelling complicated problems, and the construction of high order approximations without the addition of extra nodes.…”
Section: Introductionmentioning
confidence: 99%