Gabriele Santin
Dr.
Gabriele
Santin


Phone 
0049 711 68565295

Room 
7.118 
Email address 
Link

Address

University of Stuttgart
Institute of Applied Analysis and Numerical Simulation
Pfaffenwaldring 57
D70569
Stuttgart
Germany

Research Interests
I work in the field of kernelbased approximation methods.
My current interest is in greedy algorithms for scalar and vectorial data, with applications
to surrogate models.
Preprints
De Marchi, S.; Iske, A. & Santin, G.:
Image Reconstruction from Scattered Radon Data by Weighted Positive Definite Kernel Functions,
2017.
@techreport
{DeMarchi2017,
author = {De Marchi,Stefano and Iske, Armin and Santin, Gabriele}
,
title = {Image Reconstruction from Scattered Radon Data by Weighted Positive Definite Kernel Functions}
,
year = {2017}
}
Haasdonk, B. & Santin, G.:
Greedy Kernel Approximation for Sparse Surrogate Modelling,
2017.
@techreport
{HS2017a,
author = {B. Haasdonk and G. Santin}
,
title = {Greedy Kernel Approximation for Sparse Surrogate Modelling}
,
year = {2017}
}
Publications
Santin, G. & Haasdonk, B.:
Convergence rate of the dataindependent Pgreedy algorithm in kernelbased approximation,
Dolomites Research Notes on Approximation,
2017, 10, 6878.
@article
{SH16b,
author = {G. Santin and B. Haasdonk}
,
title = {Convergence rate of the dataindependent Pgreedy algorithm in kernelbased approximation}
,
journal = {Dolomites Research Notes on Approximation}
,
year = {2017}
,
volume = {10}
,
pages = {6878}
,
url = {http://www.emis.de/journals/DRNA/92.html}
}
Abstract: Kernelbased methods provide flexible and accurate algorithms for the reconstruction of functions from meshless samples. A major question in the use of such methods is the influence of the samples’ locations on the behavior of the approximation, and feasible optimal strategies are not known for general problems. Nevertheless, efficient and greedy pointselection strategies are known. This paper gives a proof of the convergence rate of the dataindependent $P$greedy algorithm, based on the application of the convergence theory for greedy algorithms in reduced basis methods. The resulting rate of convergence is shown to be quasioptimal in the case of kernels generating Sobolev spaces. As a consequence, this convergence rate proves that, for kernels of Sobolev spaces, the points selected by the algorithm are asymptotically uniformly distributed, as conjectured in the paper where the algorithm has been introduced
Cavoretto, R.; De Marchi, S.; De Rossi, A.; Perracchione, E. & Santin, G.:
Approximating basins of attraction for dynamical systems via stable radial bases,
AIP Conf. Proc.,
2016.
@inproceedings
{Cavoretto2016a,
author = {Cavoretto, Roberto and De Marchi, Stefano and De Rossi, Alessandra and Perracchione, Emma and Santin, Gabriele}
,
title = {Approximating basins of attraction for dynamical systems via stable radial bases}
,
booktitle = {AIP Conf. Proc.}
,
year = {2016}
,
url = {http://dx.doi.org/10.1063/1.4952177}
,
doi = {10.1063/1.4952177}
}
Abstract: In applied sciences it is often required to model and supervise temporal evolution of populations via dynamical systems. In this paper, we focus on the problem of approximating the basins of attraction of such models for each stable equilibrium point. We propose to reconstruct the basins via an implicit interpolant using stable radial bases, obtaining the surfaces by partitioning the phase space into disjoint regions. An application to a competition model presenting jointly three stable equilibria is considered.
Cavoretto, R.; De Marchi, S.; De Rossi, A.; Perracchione, E. & Santin, G.:
Partition of unity interpolation using stable kernelbased techniques,
Applied Numerical Mathematics,
2016.
@article
{Cavoretto2016,
author = {Cavoretto, Roberto and De Marchi, Stefano and De Rossi, Alessandra and Perracchione, Emma and Santin, Gabriele}
,
title = {Partition of unity interpolation using stable kernelbased techniques}
,
journal = {Applied Numerical Mathematics}
,
year = {2016}
,
url = {http://dx.doi.org/10.1016/j.apnum.2016.07.005}
,
doi = {10.1016/j.apnum.2016.07.005}
}
Abstract: In this paper we propose a new stable and accurate approximation technique which is extremely effective for interpolating large scattered data sets. The Partition of Unity (PU) method is performed considering Radial Basis Functions (RBFs) as local approximants and using locally supported weights. In particular, the approach consists in computing, for each PU subdomain, a stable basis. Such technique, taking advantage of the local scheme, leads to a significant benefit in terms of stability, especially for flat kernels. Furthermore, an optimized searching procedure is applied to build the local stable bases, thus rendering the method more efficient.
Santin, G. & Schaback, R.:
Approximation of eigenfunctions in kernelbased spaces,
Adv. Comput. Math.,
2016, 42, 973993.
@article
{Santin2016a,
author = {Santin, Gabriele and Schaback, Robert}
,
title = {Approximation of eigenfunctions in kernelbased spaces}
,
journal = {Adv. Comput. Math.}
,
year = {2016}
,
volume = {42}
,
number = {4}
,
pages = {973993}
,
url = {http://dx.doi.org/10.1007/s1044401594495}
,
doi = {10.1007/s1044401594495}
}
Abstract: Kernelbased methods in Numerical Analysis have the advantage of yielding optimal recovery processes in the ``native'' Hilbert space ℋ backslashmathcal H in which they are reproducing. Continuous kernels on compact domains have an expansion into eigenfunctions that are both L 2orthonormal and orthogonal in ℋ backslashmathcal H (Mercer expansion). This paper examines the corresponding eigenspaces and proves that they have optimality properties among all other subspaces of ℋ backslashmathcal H . These results have strong connections to nwidths in Approximation Theory, and they establish that errors of optimal approximations are closely related to the decay of the eigenvalues. Though the eigenspaces and eigenvalues are not readily available, they can be well approximated using the standard ndimensional subspaces spanned by translates of the kernel with respect to n nodes or centers. We give error bounds for the numerical approximation of the eigensystem via such subspaces. A series of examples shows that our numerical technique via a greedy point selection strategy allows to calculate the eigensystems with good accuracy.
Cavoretto, R.; De Marchi, S.; De Rossi, A.; Perracchione, E. & Santin, G.:
VigoAguiar, J. (Eds.),
RBF approximation of large datasets by partition of unity and local stabilization,
CMMSE 2015 : Proceedings of the 15th International Conference on Mathematical Methods in Science and Engineering,
2015, 317326.
@inproceedings
{Cavoretto2015,
author = {Cavoretto, Roberto and De Marchi, Stefano and De Rossi, Alessandra and Perracchione, Emma and Santin, Gabriele}
,
editor = {VigoAguiar, J.}
,
title = {RBF approximation of large datasets by partition of unity and local stabilization}
,
booktitle = {CMMSE 2015 : Proceedings of the 15th International Conference on Mathematical Methods in Science and Engineering}
,
year = {2015}
,
pages = {317326}
}
Abstract: We present an algorithm to approximate large dataset by Radial Basis Function (RBF) techniques. The method couples a fast domain decomposition procedure with a localized stabilization method. The resulting algorithm can eciently deal with large problems and it is robust with respect to the typical instability of kernel methods.
De Marchi, S. & Santin, G.:
Fast computation of orthonormal basis for RBF spaces through Krylov space methods,
BIT Numerical Mathematics,
Springer Netherlands,
2015, 55, 949966.
@article
{DeMarchi2015a,
author = {De Marchi, Stefano and Santin, Gabriele}
,
title = {Fast computation of orthonormal basis for RBF spaces through Krylov space methods}
,
journal = {BIT Numerical Mathematics}
,
publisher = {Springer Netherlands}
,
year = {2015}
,
volume = {55}
,
number = {4}
,
pages = {949966}
,
url = {http://dx.doi.org/10.1007/s1054301405376}
,
doi = {10.1007/s1054301405376}
}
Abstract: In recent years, in the setting of radial basis function, the study of approximation algorithms has particularly focused on the construction of (stable) bases for the associated Hilbert spaces. One of the ways of describing such spaces and their properties is the study of a particular integral operator and its spectrum. We proposed in a recent work the socalled WSVD basis, which is strictly connected to the eigendecomposition of this operator and allows to overcome some problems related to the stability of the computation of the approximant for a wide class of radial kernels. Although effective, this basis is computationally expensive to compute. In this paper we discuss a method to improve and compute in a fast way the basis using methods related to Krylov subspaces. After reviewing the connections between the two bases, we concentrate on the properties of the new one, describing its behavior by numerical tests.
De Marchi, S. & Santin, G.:
A new stable basis for radial basis function interpolation,
J. Comput. Appl. Math.,
2013, 253, 113.
@article
{DeMarchi2013,
author = {De Marchi, Stefano and Santin, Gabriele}
,
title = {A new stable basis for radial basis function interpolation}
,
journal = {J. Comput. Appl. Math.}
,
year = {2013}
,
volume = {253}
,
pages = {113}
,
url = {http://dx.doi.org/10.1016/j.cam.2013.03.048}
,
doi = {10.1016/j.cam.2013.03.048}
}
Abstract: Abstract It is well known that radial basis function interpolants suffer from bad conditioning if the basis of translates is used. In the recent work by Pazouki and Schaback (2011), [5], the authors gave a quite general way to build stable and orthonormal bases for the native space N Φ ( Ω ) associated to a kernel Φ on a domain Ω ⊂ R s . The method is simply based on the factorization of the corresponding kernel matrix. Starting from that setting, we describe a particular basis which turns out to be orthonormal in N Φ ( Ω ) and in ℓ 2 , w ( X ) , where X is a set of data sites of the domain Ω . The basis arises from a weighted singular value decomposition of the kernel matrix. This basis is also related to a discretization of the compact operator T Φ : N Φ ( Ω ) → N Φ ( Ω ) , T Φ [ f ] ( x ) = ∫ Ω Φ ( x , y ) f ( y ) d y ∀ x ∈ Ω , and provides a connection with the continuous basis that arises from an eigendecomposition of T Φ . Finally, using the eigenvalues of this operator, we provide convergence estimates and stability bounds for interpolation and discrete leastsquares approximation.
Santin, G.; Sommariva, A. & Vianello, M.:
An algebraic cubature formula on curvilinear polygons,
Applied Mathematics and Computation,
2011, 217, 1000310015.
@article
{Santin2011,
author = {Santin, Gabriele and Sommariva, Alvise and Vianello, Marco}
,
title = {An algebraic cubature formula on curvilinear polygons}
,
journal = {Applied Mathematics and Computation}
,
year = {2011}
,
volume = {217}
,
number = {24}
,
pages = {1000310015}
,
url = {http://dx.doi.org/10.1016/j.amc.2011.04.071}
,
doi = {10.1016/j.amc.2011.04.071}
}
Abstract: We implement in Matlab a Gausslike cubature formula on bivariate domains whose boundary is a piecewise smooth Jordan curve (curvilinear polygons). The key tools are Green’s integral formula, together with the recent software package Chebfun to approximate the boundary curve close to machine precision by piecewise Chebyshev interpolation. Several tests are presented, including some comparisons of this new routine ChebfunGauss with the recent SplineGauss that approximates the boundary by splines.
Thesis
Santin, G.:
Approximation in kernelbased spaces, optimal subspaces and approximation of eigenfunction,
Doctoral School in Mathematical Sciences, University of Padova,
2016.
@phdthesis
{Santin2016Th,
author = {Santin, Gabriele}
,
title = {Approximation in kernelbased spaces, optimal subspaces and approximation of eigenfunction}
,
school = {Doctoral School in Mathematical Sciences, University of Padova}
,
year = {2016}
,
url = {http://paduaresearch.cab.unipd.it/9186/}
}
Teaching
Talks
13.9.2016
Nonsymmetric kernelbased approximation,
DWCAA 2016
30.3.2016
Greedy Kernel Interpolation Surrogate Modeling (Poster),
MORML 2016
610.7.2015
RBF approximation of large datasets by partition of unity and local stabilization,
CMMSE2015
25.9.2014
Approximation in kernel based spaces,
SPAN
813.9.2013
WSVD basis for RBF and Krylov subspaces (Poster),
DRWA13
59.8.2013
A orthonormal basis for Radial Basis Function approximation ,
Isaac 9th Congress
915.6.2013
A fast algorithm for computing a truncated orthonormal basis for RBF native spaces ,
CTF2013
914.9.2012
A new stable basis for RBF approximation (Poster),
DWCAA2012
Further information
My profiles on
ORCID,
Google Scholar,
Research Gate.
Software
2016
Pgreedy:implementation
of the Pgreedy algorithm (MATLAB).
2015
EigenApprox:Approximation
of eigenfunctions in kernel based spaces (MATLAB).
2015
KBMIR:Kernel
based medical image reconstruction (MATLAB).
2014
WSVD
and FCoOB:RBF Approximation with WSVDBasis and Fast WSVDBasis (MATLAB).