44 research outputs found
Rational RBF-based partition of unity method for efficiently and accurately approximating 3D objects
We consider the problem of reconstructing 3D objects via meshfree
interpolation methods. In this framework, we usually deal with large data sets
and thus we develop an efficient local scheme via the well-known Partition of
Unity (PU) method. The main contribution in this paper consists in constructing
the local interpolants for the implicit interpolation by means of Rational
Radial Basis Functions (RRBFs). Numerical evidence confirms that the proposed
method is particularly performing when 3D objects, or more in general implicit
functions defined by scattered data, need to be approximated
Fast and flexible interpolation via PUM with applications in population dynamics
In this paper the Partition of Unity Method (PUM) is efficiently performed
using Radial Basis Functions (RBFs) as local approximants. In particular, we
present a new space-partitioning data structure extremely useful in
applications because of its independence from the problem geometry. Moreover,
we study, in the context of wild herbivores in forests, an application of such
algorithm. This investigation shows that the ecosystem of the considered
natural park is in a very delicate situation, for which the animal population
could become extinguished. The determination of the so-called sensitivity
surfaces, obtained with the new fast and flexible interpolation tool, indicates
some possible preventive measures to the park administrators
Partition of Unity Interpolation on Multivariate Convex Domains
In this paper we present a new algorithm for multivariate interpolation of
scattered data sets lying in convex domains \Omega \subseteq \RR^N, for any
. To organize the points in a multidimensional space, we build a
-tree space-partitioning data structure, which is used to efficiently apply
a partition of unity interpolant. This global scheme is combined with local
radial basis function approximants and compactly supported weight functions. A
detailed description of the algorithm for convex domains and a complexity
analysis of the computational procedures are also considered. Several numerical
experiments show the performances of the interpolation algorithm on various
sets of Halton data points contained in , where can be any
convex domain like a 2D polygon or a 3D polyhedron
Recursive POD expansion for the advection-diffusion-reaction equation
This paper deals with the approximation of advection-diffusion-reaction
equation solution by reduced order methods. We use the Recursive POD approximation for multivariate functions introduced in [M. AZAÏEZ, F. BEN BELGACEM, T. CHACÓN REBOLLO, Recursive POD expansion for reactiondiffusion equation, Adv.Model. and Simul. in Eng. Sci. (2016) 3:3. DOI 10.1186/s40323-016-0060-1] and applied to the low tensor representation
of the solution of the reaction-diffusion partial differential equation. In this
contribution we extend the Recursive POD approximation for multivariate functions with an arbitrary number of parameters, for which we prove general error estimates. The method is used to approximate the solutions of the advection-diffusion-reaction equation. We prove spectral error estimates, in which the spectral convergence rate depends only on the diffusion interval, while the error estimates are affected by a factor that grows exponentially with the advection velocity, and are independent of the reaction rate if this lives in a bounded set. These error estimates are based upon the analyticity of the solution of these equations as a function of the parameters (advection
velocity, diffusion, reaction rate). We present several numerical tests, strongly consistent with the theoretical error estimates.Ministerio de Economía y CompetitividadAgence nationale de la rechercheGruppo Nazionale per il Calcolo ScientificoUE ERA-PLANE
Recursive POD expansion for the advection-diffusion-reaction equation
This paper deals with the approximation of advection-diffusion-reaction
equation solution by reduced order methods. We use the Recursive POD approximation for multivariate functions introduced in [M. AZAÏEZ, F. BEN BELGACEM, T. CHACÓN REBOLLO, Recursive POD expansion for reactiondiffusion equation, Adv.Model. and Simul. in Eng. Sci. (2016) 3:3. DOI 10.1186/s40323-016-0060-1] and applied to the low tensor representation
of the solution of the reaction-diffusion partial differential equation. In this
contribution we extend the Recursive POD approximation for multivariate functions with an arbitrary number of parameters, for which we prove general error estimates. The method is used to approximate the solutions of the advection-diffusion-reaction equation. We prove spectral error estimates, in which the spectral convergence rate depends only on the diffusion interval, while the error estimates are affected by a factor that grows exponentially with the advection velocity, and are independent of the reaction rate if this lives in a bounded set. These error estimates are based upon the analyticity of the solution of these equations as a function of the parameters (advection
velocity, diffusion, reaction rate). We present several numerical tests, strongly consistent with the theoretical error estimates.Ministerio de Economía y CompetitividadAgence nationale de la rechercheGruppo Nazionale per il Calcolo ScientificoUE ERA-PLANE
A new numerical method for processing longitudinal data: clinical applications
Background: Processing longitudinal data is a computational issue that arises in many applications, such as in aircraft design, medicine, optimal control and weather forecasting. Given some longitudinal data, i.e. scattered measurements, the aim consists in approximating the parameters involved in the dynamics of the considered process. For this problem, a large variety of well-known methods have already been developed.
Results: Here, we propose an alternative approach to be used as effective and accurate tool for the parameters fitting and prediction of individual trajectories from sparse longitudinal data. In particular, our mixed model, that uses Radial Basis Functions (RBFs) combined with Stochastic Optimization Algorithms (SOMs), is here presented and tested on clinical data. Further, we also carry out comparisons with other methods that are widely used in this framework.
Conclusions: The main advantages of the proposed method are the flexibility with respect to the datasets, meaning that it is effective also for truly irregularly distributed data, and its ability to extract reliable information on the evolution of the dynamics