1,426 research outputs found
Dimensions and bases of hierarchical tensor-product splines
International audienceWe prove that the dimension of trivariate tensor-product spline space of tri-degree (m,m,m) with maximal order of smoothness over a three- dimensional domain coincides with the number of tensor-product B-spline basis functions acting effectively on the domain considered. A domain is required to belong to a certain class. This enables us to show that, for a cer- tain assumption about the configuration of a hierarchical mesh, hierarchical B-splines span the spline space. This paper presents an extension to three-dimensional hierarchical meshes of results proposed recently by Giannelli and Ju ̈ttler for two-dimensional hierarchical meshes
Bases of T-meshes and the refinement of hierarchical B-splines
In this paper we consider spaces of bivariate splines of bi-degree (m, n)
with maximal order of smoothness over domains associated to a two-dimensional
grid. We define admissible classes of domains for which suitable combinatorial
technique allows us to obtain the dimension of such spline spaces and the
number of tensor-product B-splines acting effectively on these domains.
Following the strategy introduced recently by Giannelli and Juettler, these
results enable us to prove that under certain assumptions about the
configuration of a hierarchical T-mesh the hierarchical B-splines form a basis
of bivariate splines of bi-degree (m, n) with maximal order of smoothness over
this hierarchical T-mesh. In addition, we derive a sufficient condition about
the configuration of a hierarchical T-mesh that ensures a weighted partition of
unity property for hierarchical B-splines with only positive weights
Sum-factorization techniques in Isogeometric Analysis
The fast assembling of stiffness and mass matrices is a key issue in
isogeometric analysis, particularly if the spline degree is increased. We
present two algorithms based on the idea of sum factorization, one for matrix
assembling and one for matrix-free methods, and study the behavior of their
computational complexity in terms of the spline order . Opposed to the
standard approach, these algorithms do not apply the idea element-wise, but
globally or on macro-elements. If this approach is applied to Gauss quadrature,
the computational complexity grows as instead of as
previously achieved.Comment: 34 pages, 8 figure
Bivariate hierarchical Hermite spline quasi--interpolation
Spline quasi-interpolation (QI) is a general and powerful approach for the
construction of low cost and accurate approximations of a given function. In
order to provide an efficient adaptive approximation scheme in the bivariate
setting, we consider quasi-interpolation in hierarchical spline spaces. In
particular, we study and experiment the features of the hierarchical extension
of the tensor-product formulation of the Hermite BS quasi-interpolation scheme.
The convergence properties of this hierarchical operator, suitably defined in
terms of truncated hierarchical B-spline bases, are analyzed. A selection of
numerical examples is presented to compare the performances of the hierarchical
and tensor-product versions of the scheme
Functional Regression
Functional data analysis (FDA) involves the analysis of data whose ideal
units of observation are functions defined on some continuous domain, and the
observed data consist of a sample of functions taken from some population,
sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the
development of this field, which has accelerated in the past 10 years to become
one of the fastest growing areas of statistics, fueled by the growing number of
applications yielding this type of data. One unique characteristic of FDA is
the need to combine information both across and within functions, which Ramsay
and Silverman called replication and regularization, respectively. This article
will focus on functional regression, the area of FDA that has received the most
attention in applications and methodological development. First will be an
introduction to basis functions, key building blocks for regularization in
functional regression methods, followed by an overview of functional regression
methods, split into three types: [1] functional predictor regression
(scalar-on-function), [2] functional response regression (function-on-scalar)
and [3] function-on-function regression. For each, the role of replication and
regularization will be discussed and the methodological development described
in a roughly chronological manner, at times deviating from the historical
timeline to group together similar methods. The primary focus is on modeling
and methodology, highlighting the modeling structures that have been developed
and the various regularization approaches employed. At the end is a brief
discussion describing potential areas of future development in this field
BPX-Preconditioning for isogeometric analysis
We consider elliptic PDEs (partial differential equations) in the framework of isogeometric analysis, i.e., we treat the physical domain by means of a B-spline or Nurbs mapping which we assume to be regular. The numerical solution of the PDE is computed by means of tensor product B-splines mapped onto the physical domain. We construct additive multilevel preconditioners and show that they are asymptotically optimal, i.e., the spectral condition number of the resulting preconditioned stiffness matrix is independent of . Together with a nested iteration scheme, this enables an iterative solution scheme of optimal linear complexity. The theoretical results are substantiated by numerical examples in two and three space dimensions
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