Gabriele Santin

Junior Participating Researcher, Postdoc und Lehrassistent in der Forschungsgruppe Numerische Mathematik
Institut für Angewandte Analysis und Numerische Simulation


0049 711 685-65295


Pfaffenwaldring 57
70569 Stuttgart
Raum: 7.118


  • Approximation Theory
  • Machine Learning
  • Kernel Methods
  • High dimensional approximation
  • Data-driven surrogate modelling



  1. Köppl, T.; Santin, G.; Haasdonk, B. & Helmig, R.: Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and machine learning techniques, 2017.
  2. Köppel, M.; Franzelin, F.; Kröker, I.; Oladyshkin, S.; Santin, G.; Wittwar, D.; Barth, A.; Haasdonk, B.; Nowak, W.; Pflüger, D. & Rohde, C.: Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario, 2017.



  1. Brünnette, T.; Santin, G. & Haasdonk, B.: Greedy kernel methods for accelerating implicit integrators for parametric ODEs, 2018, Proceedings of ENUMATH 2017.
  2. De Marchi, S.; Iske, A. & Santin, G.: Image reconstruction from scattered Radon data by weighted positive definite kernel functions, Calcolo, 2018, 55, 2.
  3. De Marchi, S.; Idda, A. & Santin, G.: Fasshauer, Gregory E. and Schumaker, Larry L. (Eds.), A Rescaled Method for RBF Approximation, Approximation Theory XV: San Antonio 2016, Springer International Publishing, 2017, 39-59.
  4. Haasdonk, B. & Santin, G.: Greedy Kernel Approximation for Sparse Surrogate Modelling, Proceedings of the KoMSO Challenge Workshop on Reduced-Order Modeling for Simulation and Optimization, 2017.
  5. Santin, G. & Haasdonk, B.: Convergence rate of the data-independent P-greedy algorithm in kernel-based approximation, Dolomites Research Notes on Approximation, 2017, 10, 68-78.
  6. 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.
  7. Cavoretto, R.; De Marchi, S.; De Rossi, A.; Perracchione, E. & Santin, G.: Partition of unity interpolation using stable kernel-based techniques, Applied Numerical Mathematics, 2016.
  8. Santin, G. & Schaback, R.: Approximation of eigenfunctions in kernel-based spaces, Adv. Comput. Math., 2016, 42, 973-993.
  9. Cavoretto, R.; De Marchi, S.; De Rossi, A.; Perracchione, E. & Santin, G.: Vigo-Aguiar, 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, 317-326.
  10. De Marchi, S. & Santin, G.: Fast computation of orthonormal basis for RBF spaces through Krylov space methods, BIT Numerical Mathematics, Springer Netherlands, 2015, 55, 949-966.
  11. De Marchi, S. & Santin, G.: A new stable basis for radial basis function interpolation, J. Comput. Appl. Math., 2013, 253, 1-13.
  12. Santin, G.; Sommariva, A. & Vianello, M.: An algebraic cubature formula on curvilinear polygons, Applied Mathematics and Computation, 2011, 217, 10003-10015. 


Jan. 2013 - Mär. 2016: PhD in Computermathematik, Betreuer: Prof.s S. De Marchi, R. Schaback

Okt. 2009 - Jul. 2012: MSc in Mathematik, Betreuer: Prof. S. De Marchi

Okt. 2006 - Okt. 2009: BSc in Mathematik, Betreuer: Prof.s A. Sommariva, M. Vianello



Seit Nov. 2015: PostDoc an der Universität Stuttgart, Forschungsgruppe Numerische Mathematik (Prof. B. Haasdonk)

Jan. 2013 - Okt. 2015: PhD Student in Computermathematik, Doktorandenschule in Mathematical Sciences, Forschungsgruppe Constructive Approximation and Applications (Prof. S. De Marchi)

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