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Since the advent of mesh-free methods as a tool for the numerical analysis of systems of Partial Differential Equations (PDEs), many variants of differential operator approximation have been proposed. In this work, we propose a local…

Numerical Analysis · Mathematics 2022-03-07 Mitja Jančič , Gregor Kosec

This paper proves joint convergence of the approximation error for several stochastic integrals with respect to local Brownian semimartingales, for nonequidistant and random grids. The conditions needed for convergence are that the Lebesgue…

Probability · Mathematics 2013-09-24 Carl Lindberg , Holger Rootzén

We develop efficient and high-order accurate finite difference methods for elliptic partial differential equations in complex geometry in the Difference Potentials framework. The main novelty of the developed schemes is the use of local…

Numerical Analysis · Mathematics 2023-06-28 Qing Xia

In this work, our aim is to reconstruct the unknown initial value from terminal data. We develop a numerical framework on nonuniform time grids for fractional wave equations under the lower regularity assumptions. Then, we introduce a…

Numerical Analysis · Mathematics 2025-06-25 Dakang Cen , Zhiyuan Li , Wenlong Zhang

We derive stability estimates for three commonly used radial basis function (RBF) methods to solve hyperbolic time-dependent PDEs: the RBF generated finite difference (RBF-FD) method, the RBF partition of unity method (RBF-PUM) and Kansa's…

Numerical Analysis · Mathematics 2024-08-27 Igor Tominec , Murtazo Nazarov , Elisabeth Larsson

Radial basis function methods are powerful tools in numerical analysis and have demonstrated good properties in many different simulations. However, for time-dependent partial differential equations, only a few stability results are known.…

Numerical Analysis · Mathematics 2022-04-08 Jan Glaubitz , Jan Nordström , Philipp Öffner

Our interest has been to study the effect that scattered radiation has on contrast, signal-to-noise ratio and thickness reconstruction in digital mammographies. Using the GEANT code we have performed Monte-Carlo simulations of 25 kVp Mo/Mo…

Medical Physics · Physics 2009-09-29 Varlen Grabski , Maria-Ester Brandan

Global radial basis function (RBF) collocation methods with inifinitely smooth basis functions for partial differential equations (PDEs) work in general geometries, and can have exponential convergence properties for smooth solution…

Numerical Analysis · Mathematics 2020-01-31 Elisabeth Larsson , Ulrika Sundin

The next generation of space-based telescopes used for weak lensing surveys will require exquisite point spread function (PSF) determination. Previously negligible effects may become important in the reconstruction of the PSF, in part…

Instrumentation and Methods for Astrophysics · Physics 2016-01-27 T. Kuntzer , F. Courbin , G. Meylan

This paper presents an adaptive hyperviscosity stabilisation procedure for the Radial Basis Function-generated Finite Difference (RBF-FD) method, aimed at solving linear and non-linear advection-dominated transport equations on domains…

Numerical Analysis · Mathematics 2026-04-22 Miha Rot , Žiga Vaupotič , Andrej Kolar-Požun , Gregor Kosec

We introduce a new Partition of Unity Method for the numerical homogenization of elliptic partial differential equations with arbitrarily rough coefficients. We do not restrict to a particular ansatz space or the existence of a finite…

Numerical Analysis · Mathematics 2016-05-04 Daniel Peterseim , Patrick Henning , Philipp Morgenstern

We present a novel hyperviscosity formulation for stabilizing RBF-FD discretizations of the advection-diffusion equation. The amount of hyperviscosity is determined quasi-analytically for commonly-used explicit, implicit, and…

Numerical Analysis · Mathematics 2018-08-01 Varun Shankar , Aaron L. Fogelson

A ubiquitous challenge in design space exploration or uncertainty quantification of complex engineering problems is the minimization of computational cost. A useful tool to ease the burden of solving such systems is model reduction. This…

Numerical Analysis · Mathematics 2021-04-16 Felix Newberry , Jerrad Hampton , Kenneth Jansen , Alireza Doostan

The subject of this work is an adaptive stochastic Galerkin finite element method for parametric or random elliptic partial differential equations, which generates sparse product polynomial expansions with respect to the parametric…

Numerical Analysis · Mathematics 2025-03-28 Markus Bachmayr , Martin Eigel , Henrik Eisenmann , Igor Voulis

This paper is devoted to the numerical analysis of a fully discrete finite element approximation for the stochastic Benjamin-Bona-Mahony equation driven by multiplicative noise. We first establish the existence and uniqueness of solutions…

Numerical Analysis · Mathematics 2026-03-10 Hung D. Nguyen , Thoa Thieu , Liet Vo

Numerical solutions of differential equations are usually not smooth functions. However, they should resemble the smoothness of the corresponding real solutions in one way or another. In two of our recent papers, a kind of spacial…

Numerical Analysis · Mathematics 2012-07-13 Tong Sun

In this paper, we present a block-oriented scheme for adaptive mesh refinement based on summation-by-parts (SBP) finite difference methods and simultaneous-approximation-term (SAT) interface treatment. Since the order of accuracy at SBP-SAT…

Numerical Analysis · Mathematics 2019-03-05 Anna Nissen , Katharina Kormann , Magnus Grandin , Kristoffer Virta

This paper interprets the stabilized finite element method via residual minimization as a variational multiscale method. We approximate the solution to the partial differential equations using two discrete spaces that we build on a…

Computational Engineering, Finance, and Science · Computer Science 2023-05-23 Juan F. Giraldo , Victor M. Calo

We investigate the spectrum of differentiation matrices for certain operators on the sphere that are generated from collocation at a set of scattered points $X$ with positive definite and conditionally positive definite kernels. We focus on…

Numerical Analysis · Mathematics 2023-12-27 Thomas Hangelbroek , Christian Rieger , Grady Wright

We theoretically analyzed the convergence behavior of Riemannian stochastic gradient descent (RSGD) and found that using an increasing batch size leads to faster convergence than using a constant batch size, not only with a constant…

Machine Learning · Computer Science 2025-10-14 Kanata Oowada , Hideaki Iiduka