GaussQR: Stable Gaussian computation. Welcome to the GaussQR software website, developed both to facilitate experimentation with positive definite kernels (radial basis functions) and as supplemental content for the new book Kernel-Based Approximation Methods in MATLAB available from World Scientific Press. Although we are always updating our software library, we are happy to announce the release of the GaussQR 2.0 library (previously called RBF-QR) which has a host of brand new examples designed to demonstrate topics in our book. The upgrade should be relatively painless if you have been keeping up with the repository, although some adjustment is needed if you were still running rbfqr-1.3. Please contact us if you have difficulty using this, or if inconsistencies appear.

References in zbMATH (referenced in 98 articles )

Showing results 21 to 40 of 98.
Sorted by year (citations)
  1. Nikan, O.; Avazzadeh, Z.: Coupling of the Crank-Nicolson scheme and localized meshless technique for viscoelastic wave model in fluid flow (2021)
  2. Perracchione, Emma; Massone, Anna Maria; Piana, Michele: Feature augmentation for the inversion of the Fourier transform with limited data (2021)
  3. Wenzel, Tizian; Santin, Gabriele; Haasdonk, Bernard: A novel class of stabilized greedy kernel approximation algorithms: convergence, stability and uniform point distribution (2021)
  4. Cavoretto, R.; De Rossi, A.: Adaptive refinement techniques for RBF-PU collocation (2020)
  5. Cavoretto, Roberto; De Rossi, Alessandra: A two-stage adaptive scheme based on RBF collocation for solving elliptic PDEs (2020)
  6. Cavoretto, Roberto; De Rossi, Alessandra: Adaptive procedures for meshfree RBF unsymmetric and symmetric collocation methods (2020)
  7. Cavoretto, Roberto; De Rossi, Alessandra: An adaptive LOOCV-based refinement scheme for RBF collocation methods over irregular domains (2020)
  8. Cavoretto, Roberto; De Rossi, Alessandra: Error indicators and refinement strategies for solving Poisson problems through a RBF partition of unity collocation scheme (2020)
  9. Chiu, Sung Nok; Ling, Leevan; McCourt, Michael: On variable and random shape Gaussian interpolations (2020)
  10. De Marchi, S.; Erb, W.; Marchetti, F.; Perracchione, E.; Rossini, M.: Shape-driven interpolation with discontinuous kernels: error analysis, edge extraction, and applications in magnetic particle imaging (2020)
  11. De Marchi, S.; Marchetti, F.; Perracchione, E.: Jumping with variably scaled discontinuous kernels (VSDKs) (2020)
  12. Esmaeili, H.; Moazami, D.: Application of Hilbert-Schmidt SVD approach to solve linear two-dimensional Fredholm integral equations of the second kind (2020)
  13. Esmaeili, H.; Moazami, Davoud: A stable kernel-based technique for solving linear Fredholm integral equations of the second kind and its applications (2020)
  14. Gao, Wenwu; Fasshauer, Gregory E.; Sun, Xingping; Zhou, Xuan: Optimality and regularization properties of quasi-interpolation: deterministic and stochastic approaches (2020)
  15. Hussain, Manzoor; Haq, Sirajul; Ghafoor, Abdul: Meshless RBFs method for numerical solutions of two-dimensional high order fractional Sobolev equations (2020)
  16. Karimi, N.; Kazem, S.; Ahmadian, D.; Adibi, H.; Ballestra, L. V.: On a generalized Gaussian radial basis function: analysis and applications (2020)
  17. Karvonen, Toni; Särkkä, Simo: Worst-case optimal approximation with increasingly flat Gaussian kernels (2020)
  18. Karvonen, Toni; Wynne, George; Tronarp, Filip; Oates, Chris; Särkkä, Simo: Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions (2020)
  19. Kounchev, O.; Render, H.; Tsachev, T.: On a class of (L)-splines of order 4: fast algorithms for interpolation and smoothing (2020)
  20. Reshniak, Viktor; Melnikov, Yuri: Method of Green’s potentials for elliptic PDEs in domains with random apertures (2020)