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Staff Priv.-Doz. Dr. Christian Rieger

Contact Information

Institut für Numerische Simulation
Endenicher Allee 19b
53115 Bonn
Phone: +49 228 73-69813
Office: EA19b 3.005
E-Mail: ed tod nnob-inu tod sni ta regeira tod b@foo tod de

beurlaubt für das WS18/19, Aachen:


WS 17/18

See all teachings of the group.


  1. ε\varepsilon -dimension in infinite dimensional hyperbolic cross approximation and application to parametric elliptic PDEs. D. Dũng, M. Griebel, V. N. Huy, and C. Rieger. Journal of Complexity, 46:66–89, 2018. Also available as INS Preprint No. 1703. BibTeX PDF Publisher arXiv
  2. Regularized kernel-based reconstruction in generalized Besov spaces. M. Griebel, C. Rieger, and B. Zwicknagl. Foundations of Computational Mathematics, 18(2):459–508, 2018. Also available as INS Preprint No. 1517. BibTeX PDF Publisher
  3. An inverse theorem for compact Lipschitz regions in RdR^d using localized kernel bases. T. Hangelbroek, F. J. Narcowich, C. Rieger, and J. D. Ward. Mathematics of Computation, 87:1949–1989, 2018. BibTeX Publisher arXiv
  4. Kernel-based reconstructions for parametric PDEs. R. Kempf, H. Wendland, and C. Rieger. Available as INS Preprint No. 1804., 2018. BibTeX PDF
  5. Incremental kernel based approximations for Bayesian inverse problems. C. Rieger. Available as INS Preprint No. 1807., 2018. BibTeX PDF
  6. Iterated Landweber method for radial basis functions interpolation with finite accuracy. C. Rieger. Submitted. Available as INS Preprint No. 1806., 2018. BibTeX PDF
  7. Sampling inequalities for anisotropic tensor product grids. C. Rieger and H. Wendland. Available as INS Preprint No. 1805., 2018. BibTeX PDF
  8. A representer theorem for deep kernel learning. B. Bohn, M. Griebel, and C. Rieger. Submitted to Journal of Machine Learning Research. Also available as INS Preprint No. 1714, 2017. BibTeX PDF
  9. Reproducing kernel Hilbert spaces for parametric partial differential equations. M. Griebel and C. Rieger. SIAM/ASA J. Uncertainty Quantification, 5:111–137, 2017. also available as INS Preprint No. 1511. BibTeX PDF Publisher
  10. Upwind schemes for scalar advection-dominated problems in the discrete exterior calculus. M. Griebel, C. Rieger, and A. Schier. In D. Bothe and A. Reusken, editors, Transport Processes at Fluidic Interfaces, pages 145–175. Springer International Publishing, 2017. BibTeX PDF Publisher
  11. Sampling inequalities for sparse grids. C. Rieger and H. Wendland. Numerische Mathematik, 2017. Also available as INS preprint no. 1609. BibTeX PDF Publisher
  12. Spectral Approximation in Reproducing Kernel Hilbert Spaces. C. Rieger. Habilitation, Institute for Numerical Simulation, University of Bonn, 2016. BibTeX
  13. Multiscale approximation and reproducing kernel Hilbert space methods. M. Griebel, C. Rieger, and B. Zwicknagl. SIAM Journal on Numerical Analysis, 53(2):852–873, 2015. Also available as INS Preprint No. 1312. BibTeX PDF Publisher
  14. Improved exponential convergence rates by oversampling near the boundary. C. Rieger and B. Zwicknagl. Constructive Approximation, 39(2):323–341, 2014. BibTeX Publisher
  15. An inverse theorem on bounded domains for meshless methods using localized bases. T. Hangelbroek, F. J. Narcowich, C. Rieger, and J. D. Ward. ArXiv e-prints, 2014. Preprint. BibTeX arXiv
  16. Sampling inequalities and support vector machines for Galerkin type data. C. Rieger. In Meshfree Methods for Partial Differential Equations V, volume 79 of Lecture Notes in Computational Science and Engineering, pages 51–63. Springer, New York, 2011. BibTeX
  17. Sampling and stability. C. Rieger, R. Schaback, and B. Zwicknagl. In Mathematical Methods for Curves and Surfaces, volume 5862 of Lecture Notes in Computer Science, pages 347–369. Springer, New York, 2010. BibTeX
  18. Sampling inequalities for infinitely smooth functions, with applications to interpolation and machine learning. C. Rieger and B. Zwicknagl. Advances in Computational Mathematics, 32(1):103–129, 2010. BibTeX
  19. Deterministic error analysis of support vector regression and related regularized kernel methods. C. Rieger and B. Zwicknagl. Journal of Machine Learning Research, 10:2115–2132, 2009. BibTeX
  20. Approximate interpolation with applications to selecting smoothing parameters. H. Wendland and C. Rieger. Numerische Mathematik, 101:729–748, 2005. BibTeX