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We interpret a wide range of flavors of Spectral Deferred Corrections (SDC) as Runge-Kutta methods (RKM). Using Butcher series, we show that the considered class of SDC methods achieve at least order p after p iterations compared to the…

Numerical Analysis · Mathematics 2026-04-06 Eugen Bronasco , Joscha Fregin , Daniel Ruprecht , Gilles Vilmart

We establish maximal local regularity results of weak solutions or local minimizers of \[ \operatorname{div} A(x, Du)=0 \quad\text{and}\quad \min_u \int_\Omega F(x,Du)\,dx, \] providing new ellipticity and continuity assumptions on $A$ or…

Analysis of PDEs · Mathematics 2022-11-01 Peter Hästö , Jihoon Ok

This paper pursues a two-fold goal. Firstly, we aim to derive novel second-order characterizations of important robust stability properties of perturbed Karush-Kuhn-Tucker systems for a broadclass of constrained optimization problems…

Optimization and Control · Mathematics 2020-04-15 Ashkan Mohammadi , Boris Mordukhovich , Ebrahim Sarabi

We present unconditionally energy stable Runge-Kutta (RK) discontinuous Galerkin (DG) schemes for solving a class of fourth order gradient flows. Our algorithm is geared toward arbitrarily high order approximations in both space and time,…

Numerical Analysis · Mathematics 2021-01-05 Hailiang Liu , Peimeng Yin

The Runge--Kutta (RK) discontinuous Galerkin (DG) method is a mainstream numerical algorithm for solving hyperbolic equations. In this paper, we use the linear advection equation in one and two dimensions as a model problem to prove the…

Numerical Analysis · Mathematics 2024-10-02 Zheng Sun

The existence of weak solutions and upper bounds for the blow-up time for time-discrete parabolic-elliptic Keller-Segel models for chemotaxis in the two-dimensional whole space are proved. For various time discretizations, including the…

Analysis of PDEs · Mathematics 2017-09-13 Ansgar Jüngel , Oliver Leingang

Deep neural networks have achieved state-of-the-art performance in a variety of fields. Recent works observe that a class of widely used neural networks can be viewed as the Euler method of numerical discretization. From the numerical…

Computer Vision and Pattern Recognition · Computer Science 2020-10-21 Byungjoo Kim , Bryce Chudomelka , Jinyoung Park , Jaewoo Kang , Youngjoon Hong , Hyunwoo J. Kim

Let $A = -{\rm div} \,a(\cdot) \nabla$ be a second order divergence form elliptic operator on $\R^n$ with bounded measurable real-valued coefficients and let $W$ be a cylindrical Brownian motion in a Hilbert space $H$. Our main result…

Classical Analysis and ODEs · Mathematics 2014-02-21 Pascal Auscher , Jan van Neerven , Pierre Portal

For the approximation of solutions for It\^o and Stratonovich stochastic differential equations (SDEs)a new class of efficient stochastic Runge-Kutta (SRK) methods is developed. As the main novelty only two stages are necessary for the…

Numerical Analysis · Mathematics 2025-07-01 Andreas Rößler

An efficient multigrid framework is developed for the time marching of steady-state compressible flows with a spatially high-order ($p$-order polynomial) modal discontinuous Galerkin method. The core algorithm that based on a global…

Computational Physics · Physics 2018-07-04 Shu-Jie Li

Explicit Runge-Kutta schemes with large stable step sizes are developed for integration of high order spectral difference spatial discretization on quadrilateral grids. The new schemes permit an effective time step that is substantially…

Numerical Analysis · Mathematics 2013-07-16 M. Parsani , D. I. Ketcheson , W. Deconinck

The paper describes the construction of entropy-stable discontinuous Galerkin difference (DGD) discretizations for hyperbolic conservation laws on unstructured grids. The construction takes advantage of existing theory for entropy-stable…

Numerical Analysis · Mathematics 2021-03-08 Ge Yan , Sharanjeet Kaur , Jeffery W. Banks , Jason E. Hicken

We study Runge-Kutta methods for rough differential equations which can be used to calculate solutions to stochastic differential equations driven by processes that are rougher than a Brownian motion. We use a Taylor series representation…

Numerical Analysis · Mathematics 2020-03-31 Martin Redmann , Sebastian Riedel

We consider a control constrained parabolic optimal control problem and use variational discretization for its time semi-discretization. The state equation is treated with a Petrov-Galerkin scheme using a piecewise constant Ansatz for the…

Optimization and Control · Mathematics 2015-03-09 Nikolaus von Daniels , Michael Hinze , Morten Vierling

Results on unconditional convergence in the Maximum norm for ADI-type methods, such as the Douglas method, applied to the time integration of semilinear parabolic problems are quite difficult to get, mainly when the number of space…

Numerical Analysis · Mathematics 2021-02-25 S. Gonzalez Pinto , D. Hernandez Abreu

We decide the stability and compute the Lyapunov exponent of continuous-time linear switching systems with a guaranteed dwell time. The main result asserts that the discretization method with step size~$h$ approximates the Lyapunov exponent…

Dynamical Systems · Mathematics 2024-02-08 Thomas Mejstrik , Vladimir Yu. Protasov

In this paper we develop a novel two-stage fourth order time-accurate discretization for time-dependent flow problems, particularly for hyperbolic conservation laws. Different from the classical Runge-Kutta (R-K) temporal discretization for…

Numerical Analysis · Mathematics 2015-12-14 Jiequan Li , Zhifang Du

In this paper, we prove convergence rates for time discretisation schemes for semi-linear stochastic evolution equations with additive or multiplicative Gaussian noise, where the leading operator $A$ is the generator of a strongly…

Numerical Analysis · Mathematics 2024-12-19 Katharina Klioba , Mark Veraar

We study the strong $L^p$-convergence rates of the Euler-Maruyama method for stochastic differential equations driven by Brownian motion with low-regularity drift coefficients. Specifically, the drift is assumed to be in the…

Probability · Mathematics 2025-08-15 Jinlong Wei , Junhao Hu , Guangying Lv , Chenggui Yuan

Finite differences and Runge-Kutta time stepping schemes used in Computational AeroAcoustics simulations are often optimized for low dispersion and dissipation (e.g. DRP or LDDRK schemes) when applied to linear problems in order to…

Numerical Analysis · Mathematics 2019-12-02 Aldaïr Petronilia , Edward James Brambley