Vesa Kaarnioja, D.Sc. (Tech.)
Free University of Berlin
Department of Mathematics and Computer Science
Arnimallee 6, 14195 Berlin, Germany

Publications

Submitted manuscripts
  1. L. Bazahica, V. Kaarnioja, and L. Roininen. Uncertainty quantification for electrical impedance tomography using quasi-Monte Carlo methods.
  2. P. A. Guth and V. Kaarnioja. Quasi-Monte Carlo for partial differential equations with generalized Gaussian input uncertainty.
  3. V. Kaarnioja and C. Schillings. Quasi-Monte Carlo for Bayesian design of experiment problems governed by parametric PDEs.
  4. I. H. Sloan and V. Kaarnioja. Doubling the rate – improved error bounds for orthogonal projection with application to interpolation.
Peer-reviewed articles
  1. V. Kaarnioja and A. Rupp. Quasi-Monte Carlo and discontinuous Galerkin. Accepted for publication in Electronic Transactions on Numerical Analysis, 2024 (preprint).
  2. V. Kaarnioja, F. Y. Kuo, and I. H. Sloan. Lattice-based kernel approximation and serendipitous weights for parametric PDEs in very high dimensions. In: Monte Carlo and Quasi-Monte Carlo Methods 2022, A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.), Springer Verlag, pp. 81-103, 2024.
  3. P. A. Guth and V. Kaarnioja. Application of dimension truncation error analysis to high-dimensional function approximation in uncertainty quantification. In: Monte Carlo and Quasi-Monte Carlo Methods 2022, A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.), Springer Verlag, pp. 297-312, 2024.
  4. P. A. Guth and V. Kaarnioja. Generalized dimension truncation error analysis for high-dimensional numerical integration: lognormal setting and beyond. SIAM Journal on Numerical Analysis 62(2), 872-892, 2024.
  5. P. A. Guth, V. Kaarnioja, F. Y. Kuo, C. Schillings, and I. H. Sloan. Parabolic PDE-constrained optimal control under uncertainty with entropic risk measure using quasi-Monte Carlo integration. Numerische Mathematik 156, 565-608, 2024.
  6. H. Hakula, H. Harbrecht, V. Kaarnioja, F. Y. Kuo, and I. H. Sloan. Uncertainty quantification for random domains using periodic random variables. Numerische Mathematik 156, 273-317, 2024.
  7. V. Kaarnioja, Y. Kazashi, F. Y. Kuo, F. Nobile, and I. H. Sloan. Fast approximation by periodic kernel-based lattice-point interpolation with application in uncertainty quantification. Numerische Mathematik 150, 33-77, 2022.
  8. P. A. Guth, V. Kaarnioja, F. Y. Kuo, C. Schillings, and I. H. Sloan. A quasi-Monte Carlo method for optimal control under uncertainty. SIAM/ASA Journal on Uncertainty Quantification 9(2), 354-383, 2021.
  9. V. Kaarnioja. Bounds on the spectrum of nonsingular triangular (0,1)-matrices. Journal of Combinatorial Theory, Series A 178, 105353, 2021.
  10. V. Kaarnioja, F. Y. Kuo, and I. H. Sloan. Uncertainty quantification using periodic random variables. SIAM Journal on Numerical Analysis 58(2), 1068-1091, 2020.
  11. H. Hakula, V. Kaarnioja, and M. Laaksonen. Cylindrical shell with junctions: Uncertainty quantification of free vibration and frequency response analysis. Shock and Vibration, vol. 2018, Article ID 5817940, 16 pp., 2018.
  12. P. Ilmonen and V. Kaarnioja. Generalized eigenvalue problems for meet and join matrices on semilattices. Linear Algebra and its Applications 536, 250-273, 2018.
  13. N. Hyvönen, V. Kaarnioja, L. Mustonen, and S. Staboulis. Polynomial collocation for handling an inaccurately known measurement configuration in electrical impedance tomography. SIAM Journal on Applied Mathematics 77, 202-223, 2017.
  14. H. Hakula, V. Kaarnioja, and M. Laaksonen. Approximate methods for stochastic eigenvalue problems. Applied Mathematics and Computation 267, 664-681, 2015.
Theses