| Digital proximity tracing on empirical contact networks for pandemic control G Cencetti, G Santin, A Longa, E Pigani, A Barrat, C Cattuto, S Lehmann, ... Nature communications 12 (1), 1655, 2021 | 145* | 2021 |
| A new stable basis for radial basis function interpolation S De Marchi, G Santin Journal of Computational and Applied Mathematics 253, 1-13, 2013 | 94 | 2013 |
| Graph neural networks for temporal graphs: State of the art, open challenges, and opportunities A Longa, V Lachi, G Santin, M Bianchini, B Lepri, P Lio, F Scarselli, ... arXiv preprint arXiv:2302.01018, 2023 | 80 | 2023 |
| Convergence rate of the data-independent -greedy algorithm in kernel-based approximation G Santin, B Haasdonk arXiv preprint arXiv:1612.02672, 2016 | 76 | 2016 |
| Approximation of eigenfunctions in kernel-based spaces G Santin, R Schaback Advances in Computational Mathematics 42 (4), 973-993, 2016 | 65 | 2016 |
| A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution T Wenzel, G Santin, B Haasdonk Journal of Approximation Theory 262, 105508, 2021 | 45 | 2021 |
| Partition of unity interpolation using stable kernel-based techniques R Cavoretto, S De Marchi, A De Rossi, E Perracchione, G Santin Applied Numerical Mathematics 116, 95-107, 2017 | 44 | 2017 |
| Kernel Methods for Surrogate Modeling G Santin, B Haasdonk https://arxiv.org/abs/1907.10556, 2019 | 39 | 2019 |
| Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates for f-, - and f/P-Greedy T Wenzel, G Santin, B Haasdonk Constructive Approximation 57 (1), 45-74, 2023 | 36 | 2023 |
| Explaining the explainers in graph neural networks: a comparative study A Longa, S Azzolin, G Santin, G Cencetti, P Liò, B Lepri, A Passerini ACM Computing Surveys 57 (5), 1-37, 2025 | 35 | 2025 |
| Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario M Köppel, F Franzelin, I Kröker, S Oladyshkin, G Santin, D Wittwar, ... Computational Geosciences 23, 339-354, 2019 | 35 | 2019 |
| Fast computation of orthonormal basis for RBF spaces through Krylov space methods S De Marchi, G Santin BIT Numerical Mathematics 55, 949-966, 2015 | 34 | 2015 |
| Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods T Koeppl, G Santin, B Haasdonk, R Helmig International journal for numerical methods in biomedical engineering 34 (8 …, 2018 | 32 | 2018 |
| Kernel methods for center manifold approximation and a weak data-based version of the center manifold theorem B Haasdonk, B Hamzi, G Santin, D Wittwar Physica D: Nonlinear Phenomena 427, 133007, 2021 | 25 | 2021 |
| Interpolation with uncoupled separable matrix-valued kernels D Wittwar, G Santin, B Haasdonk arXiv preprint arXiv:1807.09111, 2018 | 25 | 2018 |
| Greedy kernel methods for center manifold approximation B Haasdonk, B Hamzi, G Santin, D Wittwar Spectral and High Order Methods for Partial Differential Equations ICOSAHOM …, 2020 | 23 | 2020 |
| Greedy kernel approximation for sparse surrogate modeling B Haasdonk, G Santin Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful …, 2018 | 20 | 2018 |
| An algebraic cubature formula on curvilinear polygons G Santin, A Sommariva, M Vianello Applied mathematics and computation 217 (24), 10003-10015, 2011 | 18 | 2011 |
| A rescaled method for RBF approximation S De Marchi, A Idda, G Santin Approximation Theory XV: San Antonio 2016 15, 39-59, 2017 | 17 | 2017 |
| Measuring close proximity interactions in summer camps during the COVID-19 pandemic E Leoni, G Cencetti, G Santin, T Istomin, D Molteni, GP Picco, E Farella, ... EPJ Data Science 11 (1), 5, 2022 | 12 | 2022 |
Bernard HaasdonkProfessor for Numerical Mathematics確認したメール アドレス: mathematik.uni-stuttgart.de
