Search Results - "Kuo, Frances Y."

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  1. 1

    Application of Quasi-Monte Carlo Methods to Elliptic PDEs with Random Diffusion Coefficients: A Survey of Analysis and Implementation by Kuo, Frances Y., Nuyens, Dirk

    Published in Foundations of computational mathematics (01-12-2016)
    “…This article provides a survey of recent research efforts on the application of quasi-Monte Carlo (QMC) methods to elliptic partial differential equations…”
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  2. 2

    QUASI-MONTE CARLO FINITE ELEMENT METHODS FOR A CLASS OF ELLIPTIC PARTIAL DIFFERENTIAL EQUATIONS WITH RANDOM COEFFICIENTS by KUO, FRANCES Y., SCHWAB, CHRISTOPH, SLOAN, IAN H.

    Published in SIAM journal on numerical analysis (01-01-2012)
    “…In this paper quasi-Monte Carlo (QMC) methods are applied to a class of elliptic partial differential equations (PDEs) with random coefficients, where the…”
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  3. 3

    Multilevel Quasi-Monte Carlo methods for lognormal diffusion problems by KUO, FRANCES Y., SCHEICHL, ROBERT, SCHWAB, CHRISTOPH, SLOAN, IAN H., ULLMANN, ELISABETH

    Published in Mathematics of computation (01-11-2017)
    “…multilevel Quasi-Monte Carlo finite element discretisations and give a constructive proof of the dimension-independent convergence of the QMC rules. More…”
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  4. 4

    Multi-level Quasi-Monte Carlo Finite Element Methods for a Class of Elliptic PDEs with Random Coefficients by Kuo, Frances Y., Schwab, Christoph, Sloan, Ian H.

    Published in Foundations of computational mathematics (01-04-2015)
    “…This paper is a sequel to our previous work (Kuo et al. in SIAM J Numer Anal, 2012) where quasi-Monte Carlo (QMC) methods (specifically, randomly shifted…”
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  5. 5

    Fast random field generation with H-matrices by Feischl, Michael, Kuo, Frances Y., Sloan, Ian H.

    Published in Numerische Mathematik (01-11-2018)
    “…We use the H -matrix technology to compute the approximate square root of a covariance matrix in linear cost. This allows us to generate normal and log-normal…”
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  6. 6

    HIGHER ORDER QMC PETROV–GALERKIN DISCRETIZATION FOR AFFINE PARAMETRIC OPERATOR EQUATIONS WITH RANDOM FIELD INPUTS by DICK, JOSEF, YUO, FRANCES Y., LE GIA, QUOC T., NUYENS, DIRK, SCHWAB, CHRISTOPH

    Published in SIAM journal on numerical analysis (01-01-2014)
    “…We construct quasi–Monte Carlo methods to approximate the expected values of linear functionals of Petrov–Galerkin discretizations of parametric operator…”
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  7. 7

    Parabolic PDE-constrained optimal control under uncertainty with entropic risk measure using quasi-Monte Carlo integration by Guth, Philipp A., Kaarnioja, Vesa, Kuo, Frances Y., Schillings, Claudia, Sloan, Ian H.

    Published in Numerische Mathematik (01-04-2024)
    “…We study the application of a tailored quasi-Monte Carlo (QMC) method to a class of optimal control problems subject to parabolic partial differential equation…”
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  8. 8

    Uncertainty quantification for random domains using periodic random variables by Hakula, Harri, Harbrecht, Helmut, Kaarnioja, Vesa, Kuo, Frances Y., Sloan, Ian H.

    Published in Numerische Mathematik (01-02-2024)
    “…We consider uncertainty quantification for the Poisson problem subject to domain uncertainty. For the stochastic parameterization of the random domain, we use…”
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  9. 9

    Fast approximation by periodic kernel-based lattice-point interpolation with application in uncertainty quantification by Kaarnioja, Vesa, Kazashi, Yoshihito, Kuo, Frances Y., Nobile, Fabio, Sloan, Ian H.

    Published in Numerische Mathematik (2022)
    “…This paper deals with the kernel-based approximation of a multivariate periodic function by interpolation at the points of an integration lattice—a setting…”
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  10. 10

    Lattice meets lattice: Application of lattice cubature to models in lattice gauge theory by Hartung, Tobias, Jansen, Karl, Kuo, Frances Y., Leövey, Hernan, Nuyens, Dirk, Sloan, Ian H.

    Published in Journal of computational physics (15-10-2021)
    “…•Recursive integration strategies independent of the number of integration variables are proposed.•We show lattice based cubature methods that can improve…”
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  11. 11

    Circulant embedding with QMC: analysis for elliptic PDE with lognormal coefficients by Graham, Ivan G., Kuo, Frances Y., Nuyens, Dirk, Scheichl, Rob, Sloan, Ian H.

    Published in Numerische Mathematik (2018)
    “…In a previous paper (Graham et al. in J Comput Phys 230:3668–3694, 2011 ), the authors proposed a new practical method for computing expected values of…”
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  12. 12

    The ANOVA decomposition of a non-smooth function of infinitely many variables can have every term smooth by GRIEBEL, MICHAEL, KUO, FRANCES Y., SLOAN, IAN H.

    Published in Mathematics of computation (01-07-2017)
    “…The pricing problem for a continuous path-dependent option results in a path integral which can be recast into an infinite-dimensional integration problem. We…”
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  13. 13

    Correction to: Fast random field generation with H-matrices by Feischl, Michael, Kuo, Frances Y., Sloan, Ian H.

    Published in Numerische Mathematik (01-07-2019)
    “…The corrected version states the parameter range as p  ∈ 2ℕ instead of p  ∈ ℕ. The effect is to disallow non-smooth norms such as the ℓ 1 -norm for the…”
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  14. 14

    Multivariate integration for analytic functions with Gaussian kernels by KUO, FRANCES Y., SLOAN, IAN H., WOŹNIAKOWSKI, HENRYK

    Published in Mathematics of computation (01-03-2017)
    “…We study multivariate integration of analytic functions defined on ℝ𝑑. These functions are assumed to belong to a reproducing kernel Hilbert space whose…”
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  15. 15

    Periodization strategy may fail in high dimensions by Kuo, Frances Y., Sloan, Ian H., Woźniakowski, Henryk

    Published in Numerical algorithms (01-12-2007)
    “…We discuss periodization of smooth functions f of d variables for approximation of multivariate integrals. The benefit of periodization is that we may use…”
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  16. 16

    Correction to: Lattice Algorithms for Multivariate L∞ Approximation in the Worst-Case Setting by Kuo, Frances Y., Wasilkowski, Grzegorz W., Woźniakowski, Henryk

    Published in Constructive approximation (2020)
    “…We correct the expression for the worst-case error derived in [Kuo, Wasilkowski, Woźniakowski, Construct. Approx. 30 (2009), 475–493] and explain that the main…”
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  17. 17

    Note on ``The smoothing effect of integration in \mathbb{R}^d and the ANOVA decomposition by Griebel, Michael, Kuo, Frances Y., Sloan, Ian H.

    Published in Mathematics of computation (01-07-2017)
    “…82 (2013), 383-400. We first report a mistake, in that the main result Theorem 3.1, though correct, does not as claimed apply to the Asian option pricing…”
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  19. 19

    Gauss-Hermite quadratures for functions from Hilbert spaces with Gaussian reproducing kernels by Kuo, Frances Y., Woźniakowski, Henryk

    “…We study univariate integration with the Gaussian weight for a positive variance  α . This is done for the reproducing kernel Hilbert space with the Gaussian…”
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  20. 20

    Constructing lattice rules based on weighted degree of exactness and worst case error by Cools, Ronald, Kuo, Frances Y., Nuyens, Dirk

    Published in Computing (01-03-2010)
    “…Recall that an integration rule is said to have a trigonometric degree of exactness m if it integrates exactly all trigonometric polynomials of degree ≤ m . In…”
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