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Gabriele Santin

Researcher

Center for Information and Communication Technology

Fondazione Bruno Kessler

Hi!

I’m a Researcher in the MobS Lab at the Center for Information and Communication Technology of the Fondazione Bruno Kessler in Trento (IT).

I work with the RSDE and DCL labs to develop Machine Learning solutions and algorithms for the exploitation of different data sources and modeling techniques within the “City Sensing” project.

I’m also a junior participating researcher in the Cluster of Excellence Data-integrated Simulation Science, where I’m a member of the Project Network Machine Learning for Simulation.

Previously, I’ve been for four years in the group of Bernard Haasdonk at the University of Stuttgart, working on kernel based algorithms and their application to the surrogate modelling of complex simulations.

I obtained my MSc and PhD at the Department of Mathematics of the University of Padova under the supervision of Stefano De Marchi in the CAA group, where I wrote my theses on algorithmic and theoretical aspects of kernel based interpolation.

Publications

B. Haasdonk, B. Hamzi, G. Santin, D. Wittwar . Greedy kernel methods for center manifold approximation. (2019).

Preprint

G. Santin, B. Haasdonk . Kernel Methods for Surrogate Modeling. (2019).

Preprint

T. Brünnette, G. Santin, and B. Haasdonk . Greedy kernel methods for accelerating implicit integrators for parametric ODEs. Numerical Mathematics and Advanced Applications ENUMATH 2017, F.A. Radu, K. Kumar, I. Berre, J.M. Nordbotten, I.S. Pop, Eds (2019), 889-896.

DOI Preprint

D. Wittwar, G. Santin, B. Haasdonk . Interpolation with uncoupled separable matrix-valued kernels. Dolomites Res. Notes Approx. Vol. 11 (2018), 23-29.

DOI Preprint

M. Köppel, F. Franzelin, I. Kröker, G. Santin, D. Wittwar, S. Oladyshkin, A. Barth, B. Haasdonk, W. Nowak, D. Pflüger, C. Rohde . Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. Comput. Geosci. Vol. 23(2) (2018), 339-354.

DOI Preprint

G. Santin, B. Haasdonk . Greedy kernel approximation for sparse surrogate modelling.. Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful Algorithms as Key Enablers for Scientific Computing, W. Keiper, A. Milde, and S. Volkwein, Eds (2018), 21-45.

DOI Preprint

T. Köppl, G. Santin, B. Haasdonk, R. Helmig . Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods. Int. J. Numer. Meth. Biomed. Engng. Vol. 34 (2018), e3095.

DOI Preprint

R. Cavoretto, S. De Marchi, A. De Rossi, E. Perracchione, G. Santin . Partition of unity interpolation using stable kernel-based techniques. Appl. Numer. Math. Vol. 116 (2017), 95-107.

DOI Preprint

G. Santin, B. Haasdonk . Convergence rate of the data-independent P-greedy algorithm in kernel-based spaces. Dolomites Res. Notes Approx. Vol. 10 (2017), 68-78.

DOI Preprint

S. De Marchi, A. Idda, G. Santin . A rescaled method for RBF approximation. Appl. Math. Comput. Vol. 201 (2017), 39-59.

DOI Preprint

R. Cavoretto, S. De Marchi, A. De Rossi, E. Perracchione, G. Santin . Approximating basins of attraction for dynamical systems via stable radial bases. AIP Conf. Proc. Vol. 1738(1) (2016), 390003.

DOI Preprint

G. Santin, R. Schaback . Approximation of eigenfunctions in kernel-based spaces. Adv. Comput. Math. Vol. 42(4) (2016), 973-993.

DOI Preprint

R. Cavoretto, S. De Marchi, A. De Rossi, E. Perracchione, G. Santin . 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.

Preprint

S. De Marchi, G. Santin . A new stable basis for radial basis function interpolation. J. Comput. Appl. Math. Vol. 253 (2013), 1-13.

DOI Preprint

G. Santin, A. Sommariva, M. Vianello . An algebraic cubature formula on curvilinear polygons. Appl. Math. Comput. Vol. 217(24) (2011), 10003-10015.

DOI Preprint

Contact

  • Via Sommarive 18, Trento, 38123, Italy
  • Skype Me