This image shows Gabriele Santin

Gabriele Santin


Research assistant (former employee until 09/2019)
Institute of Applied Analysis and Numerical Simulation
Working Group Numerical Mathematics


Office Hours

after appointment


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.

  1. 2021

    1. M. Kohr, S. E. Mikhailov, and W. L. Wendland, “Dirichlet and transmission problems for anisotropic Stokes and Navier-Stokes systems with L∞ tensor coefficient under relaxed ellipticity condition,” Discrete Contin. Dyn. Syst., vol. 41, no. 9, Art. no. 9, 2021, doi: 10.3934/dcds.2021042.
  2. 2019

    1. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for  a carbon dioxide storage benchmark scenario,” Comput. Geosci., vol. 2, no. 23, Art. no. 23, 2019, doi:
    2. D. Seus, F. A. Radu, and C. Rohde, “A linear domain decomposition method for two-phase flow in porous media,” Numerical Mathematics and Advanced Applications ENUMATH 2017, pp. 603–614, 2019, doi:
  3. 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. [Online]. Available:
    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, Art. no. 1, Feb. 2018, doi: 10.1007/s10092-018-0247-6.
    3. J. Giesselmann, N. Kolbe, M. Lukacova-Medvidova, and N. Sfakianakis, “Existence and uniqueness of global classical solutions to a two species  cancer invasion haptotaxis model,” Accepted for publication in Discrete Contin. Dyn. Syst. Ser. B., 2018, [Online]. Available:
    4. 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. doi: 10.1007/978-3-319-75319-5_2.
    5. D. Wittwar and B. Haasdonk, “Greedy Algorithms for Matrix-Valued Kernels,” University of Stuttgart, 2018. [Online]. Available:
  4. 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. doi: 10.1007/978-3-319-59912-0_3.
    3. M. Kutter, C. Rohde, and A.-M. Sändig, “Well-posedness of a two scale model for liquid phase epitaxy with elasticity,” Contin. Mech. Thermodyn., vol. 29, no. 4, Art. no. 4, 2017, doi: 10.1007/s00161-015-0462-1.
    4. 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, [Online]. Available:
  5. 2016

    1. D. Amsallem and B. Haasdonk, “PEBL-ROM: Projection-Error Based Local Reduced-Order Models,” AMSES, Advanced Modeling and Simulation in Engineering Sciences, vol. 3, no. 6, Art. no. 6, 2016, doi: 10.1186/s40323-016-0059-7.
    2. 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, doi: 10.1016/j.apnum.2016.07.005.
    3. R. Cavoretto, S. De Marchi, A. De Rossi, E. Perracchione, and G. Santin, “Approximating basins of attraction for dynamical systems via stable  radial bases,” 2016. doi: 10.1063/1.4952177.
    4. D. Garmatter, B. Haasdonk, and B. Harrach, “A reduced Landweber Method for Nonlinear Inverse Problems,” Inverse Problems, vol. 32, no. 3, Art. no. 3, 2016, doi:
    5. M. Redeker and B. Haasdonk, “A POD-EIM reduced two-scale model for precipitation in porous media,” MCMDS, Mathematical and Computer Modelling of Dynamical Systems, 2016, doi: 10.1080/13873954.2016.1198384.
    6. G. Santin, “Approximation in kernel-based spaces, optimal subspaces and approximation  of eigenfunction,” Doctoral School in Mathematical Sciences, University of Padova, 2016. [Online]. Available:
    7. G. Santin and R. Schaback, “Approximation of eigenfunctions in kernel-based spaces,” Adv. Comput. Math., vol. 42, no. 4, Art. no. 4, 2016, doi: 10.1007/s10444-015-9449-5.
  6. 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.
  7. 2014

    1. W. L. Wendland, “Martin Costabel’s version of the trace theorem revisited,” Math. Methods Appl. Sci., vol. 37 (13), pp. 1924–1955, 2014.
  8. 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, doi: 10.1016/
  9. 2011

    1. M. Kohr, C. Pintea, and W. L. Wendland, “Dirichlet-transmission problems for general Brinkman operators  on Lipschitz and $C^1$ domains in Riemannian manifolds,” Discrete Contin. Dyn. Syst. Ser. B, vol. 15, no. 4, Art. no. 4, 2011, doi: 10.3934/dcdsb.2011.15.999.
    2. G. Santin, A. Sommariva, and M. Vianello, “An algebraic cubature formula on curvilinear polygons,” Applied Mathematics and Computation, vol. 217, no. 24, Art. no. 24, 2011, doi: 10.1016/j.amc.2011.04.071.
  10. 2008

    1. B. Haasdonk and M. Ohlberger, “Reduced basis method for finite volume approximations of parametrized  linear evolution equations,” ESAIM: M2AN, vol. 42, no. 2, Art. no. 2, Mar. 2008, doi: 10.1051/m2an:2008001.
    2. G. C. Hsiao and W. L. Wendland, Boundary integral equations, vol. 164. Berlin: Springer-Verlag, 2008, p. xx+618. doi: 10.1007/978-3-540-68545-6.
    3. A. Lalegname, A. Sändig, and G. Sewell, “Analytical and numerical treatment of a dynamic crack model,” International Journal of Fracture, vol. 152, no. 2, Art. no. 2, 2008, doi: 10.1007/s10704-008-9274-7.
  11. 2005

    1. B. Haasdonk, “Feature Space Interpretation of SVMs with Indefinite Kernels,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 4, Art. no. 4, 2005, doi:
  12. 2004

    1. B. Haasdonk, A. Halawani, and H. Burkhardt, “Adjustable invariant features by partial Haar-integration,” in Proceedings of the 17th International Conference on Pattern Recognition, 2004, vol. 2, no. 2, pp. 769–774. doi:
  • 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,


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,


25.9.2014 Approximation in kernel based spaces,


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),


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 ,


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


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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