Ph.D.

Gabriele Santin

Research assistant
Institute of Applied Analysis and Numerical Simulation
Working Group Numerical Mathematics

Contact

+49 711 685-65295
+49 711 685-65507

Pfaffenwaldring 57
70569 Stuttgart
Germany
Room: 7.118

Office Hours

after appointment

Subject

I work in the field of kernel-based approximation methods.
My current interest is in greedy algorithms for scalar and vectorial data, with applications to surrogate models.

Publlications:
  1. 2019

    1. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for  a carbon dioxide storage benchmark scenario,” Computers & Geosciences, vol. 2, no. 23, pp. 339–354, 2019.
  2. 2018

    1. T. Brünnette, G. Santin, and B. Haasdonk, “Greedy kernel methods for accelerating implicit integrators for parametric  ODEs,” 2018, vol. Proceedings of ENUMATH 2017.
    2. S. De Marchi, A. Iske, and G. Santin, “Image reconstruction from scattered Radon data by weighted positive  definite kernel functions,” Calcolo, vol. 55, no. 1, p. 2, 2018.
    3. B. Haasdonk and G. Santin, “Greedy Kernel Approximation for Sparse Surrogate Modeling,” in Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful  Algorithms as Key Enablers for Scientific Computing, W. Keiper, A. Milde, and S. Volkwein, Eds. Cham: Springer International Publishing, 2018, pp. 21--45.
  3. 2017

    1. S. De Marchi, A. Iske, and G. Santin, “Image Reconstruction from Scattered Radon Data by Weighted Positive  Definite Kernel Functions,” 2017.
    2. S. De Marchi, A. Idda, and G. Santin, “A Rescaled Method for RBF Approximation,” in Approximation Theory XV: San Antonio 2016, G. E. Fasshauer and L. L. Schumaker, Eds. Cham: Springer International Publishing, 2017, pp. 39--59.
    3. G. Santin and B. Haasdonk, “Convergence rate of the data-independent P-greedy algorithm in  kernel-based approximation,” Dolomites Research Notes on Approximation, vol. 10, pp. 68--78, 2017.
  4. 2016

    1. R. Cavoretto, S. De Marchi, A. De Rossi, E. Perracchione, and G. Santin, “Partition of unity interpolation using stable kernel-based techniques,” Applied Numerical Mathematics, 2016.
    2. R. Cavoretto, S. De Marchi, A. De Rossi, E. Perracchione, and G. Santin, “Approximating basins of attraction for dynamical systems via stable  radial bases,” in AIP Conf. Proc., 2016.
    3. G. Santin, “Approximation in kernel-based spaces, optimal subspaces and approximation  of eigenfunction,” PhD dissertation, Doctoral School in Mathematical Sciences, University of Padova, 2016.
    4. G. Santin and R. Schaback, “Approximation of eigenfunctions in kernel-based spaces,” Adv. Comput. Math., vol. 42, no. 4, pp. 973--993, 2016.
  5. 2015

    1. R. Cavoretto, S. De Marchi, A. De Rossi, E. Perracchione, and G. Santin, “RBF approximation of large datasets by partition of unity and local  stabilization,” in CMMSE 2015 : Proceedings of the 15th International Conference on  Mathematical Methods in Science and Engineering, 2015, pp. 317--326.
  6. 2013

    1. S. De Marchi and G. Santin, “A new stable basis for radial basis function interpolation,” J. Comput. Appl. Math., vol. 253, pp. 1--13, 2013.
  7. 2011

    1. G. Santin, A. Sommariva, and M. Vianello, “An algebraic cubature formula on curvilinear polygons,” Applied Mathematics and Computation, vol. 217, no. 24, pp. 10003--10015, 2011.
  • VKOGA validation and selection by log-marginal likelihood, SimTech Projektarbeit
  • Inverse Radon Transformation mit Multiskalen-Kernen, MSc in Mathematics.
  • Kernel Methods for Accelerating Implicit Integrators, BSc in Simulation Technology.
  • Interpolation mit Multiskalen-Kernen, BSc in Mathematics.
  • A comparison of some RBF interpolation methods: theory and numerics, MSc in Mathematics (at University of Padova).
  • Kernel-based medical image reconstruction from Radon data, MSc in Mathematics (at University of Padova).
26.9.2017 Greedy kernel methods for accelerating implicit integrators for parametric ODEs,

ENUMATH2017

13.9.2016 Non-symmetric kernel-based approximation,

DWCAA 2016

30.3.2016 Greedy Kernel Interpolation Surrogate Modeling (Poster),

MORML 2016

6-10.7.2015 RBF approximation of large datasets by partition of unity and local stabilization,

CMMSE2015

25.9.2014 Approximation in kernel based spaces,

SPAN

30.6 - 4.7.2014 Bases for Radial Basis Function Approximation,

First Joint International Meeting RSME-SCM-SEMA-SIMAI-UMI

29-30.11.2013 A fast algorithm for computing a truncated orthonormal basis for RBF native spaces,

Multivariate Approximation

21-22.10.2013 Some tools for fast and stable Radial Basis Function approximation with Scilab,

International CAE Conference

21-22.10.2013 Kernel methods for Radon transform (Poster),

International CAE Conference

8-13.9.2013 WSVD basis for RBF and Krylov subspaces (Poster),

DRWA13

5-9.8.2013 A orthonormal basis for Radial Basis Function approximation ,

Isaac 9th Congress

9-15.6.2013 A fast algorithm for computing a truncated orthonormal basis for RBF native spaces ,

CTF-2013

9-14.9.2012 A new stable basis for RBF approximation (Poster),

DWCAA2012

2016 P-greedy:implementation of the P-greedy 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 WSVD-Basis and Fast WSVD-Basis (MATLAB).
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