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A recently developed high-order implicit shock tracking (HOIST) framework for resolving discontinuous solutions of inviscid, steady conservation laws [41, 43] is extended to the unsteady case. Central to the framework is an optimization…

Numerical Analysis · Mathematics 2022-01-26 Andrew Shi , Per-Olof Persson , Matthew Zahr

In this paper, a class of arbitrarily high-order linear momentum-preserving and energy-preserving schemes are proposed, respectively, for solving the regularized long-wave equation. For the momentum-preserving scheme, the key idea is based…

Numerical Analysis · Mathematics 2021-12-07 Chaolong Jiang , Xu Qian , Songhe Song , Jin Cui

This work considers multirate generalized-structure additively partitioned Runge-Kutta (MrGARK) methods for solving stiff systems of ordinary differential equations (ODEs) with multiple time scales. These methods treat different partitions…

Numerical Analysis · Mathematics 2022-01-19 Steven Roberts , John Loffeld , Arash Sarshar , Carol S. Woodward , Adrian Sandu

Classical and new numerical schemes are generated using evolutionary computing. Differential Evolution is used to find the coefficients of finite difference approximations of function derivatives, and of single and multi-step integration…

Neural and Evolutionary Computing · Computer Science 2014-01-02 C. D. Erdbrink , V. V. Krzhizhanovskaya , P. M. A. Sloot

New time integration methods are proposed for simulating incompressible multiphase flow in pipelines described by the one-dimensional two-fluid model. The methodology is based on 'half-explicit' Runge-Kutta methods, being explicit for the…

Numerical Analysis · Mathematics 2019-02-19 Benjamin Sanderse , Arthur E. P. Veldman

Recently, an approach known as relaxation has been developed for preserving the correct evolution of a functional in the numerical solution of initial-value problems, using Runge-Kutta methods. We generalize this approach to multistep…

Numerical Analysis · Mathematics 2020-11-26 Hendrik Ranocha , Lajos Lóczi , David I. Ketcheson

We revisit the method of Carleman linearization for systems of ordinary differential equations with polynomial right-hand sides. This transformation provides an approximate linearization in a higher-dimensional space through the exact…

Numerical Analysis · Mathematics 2017-11-08 Marcelo Forets , Amaury Pouly

We consider second order explicit and implicit two-step time-discrete schemes for wave-type equations. We derive optimal order aposteriori estimates controlling the time discretization error. Our analysis, has been motivated by the need to…

Numerical Analysis · Mathematics 2017-05-17 Emmanuil H. Georgoulis , Omar Lakkis , Charalambos Makridakis , Juha M. Virtanen

We consider high-order splitting schemes for large-scale differential Riccati equations. Such equations arise in many different areas and are especially important within the field of optimal control. In the large-scale case, it is critical…

Optimization and Control · Mathematics 2018-08-14 Tony Stillfjord

Splitting methods constitute a well-established class of numerical schemes for the time integration of partial differential equations. Their main advantages over more traditional schemes are computational efficiency and superior geometric…

Numerical Analysis · Mathematics 2017-01-06 Lukas Einkemmer , Alexander Ostermann

We consider a general linear parabolic problem with extended time boundary conditions (including initial value problems and periodic ones), and approximate it by the implicit Euler scheme in time and the Gradient Discretisation method in…

Numerical Analysis · Mathematics 2023-08-22 J Droniou , R Eymard , T Gallouët , C Guichard , R Herbin

In this paper, we present error estimates of fully discrete Runge--Kutta discontinuous Galerkin (DG) schemes for linear time-dependent partial differential equations. The analysis applies to explicit Runge--Kutta time discretizations of any…

Numerical Analysis · Mathematics 2020-01-07 Zheng Sun , Chi-Wang Shu

In ordinary turbulence research it has been a long standing tradition to solve the equations in spectral space giving the best possible accuracy. This is indeed a natural choice for incompressible problems with periodic boundaries, but it…

Astrophysics · Physics 2009-11-07 A. Brandenburg , W. Dobler

We study the task of learning latent-variable models. A common algorithmic technique for this task is the method of moments. Unfortunately, moment-based approaches are hampered by the fact that the moment tensors of super-constant degree…

Data Structures and Algorithms · Computer Science 2025-04-15 Ilias Diakonikolas , Daniel M. Kane

We consider the numerical solution of large-scale symmetric differential matrix Riccati equations. Under certain hypotheses on the data, reduced order methods have recently arisen as a promising class of solution strategies, by forming…

Numerical Analysis · Mathematics 2020-01-14 Gerhard Kirsten , Valeria Simoncini

Exponential Runge--Kutta methods have shown to be competitive for the time integration of stiff semilinear parabolic PDEs. The current construction of stiffly accurate exponential Runge--Kutta methods, however, relies on a convergence…

Numerical Analysis · Mathematics 2020-09-29 Vu Thai Luan

When dealing with stiff conservation laws, explicit time integration forces to employ very small time steps, due to the restrictive CFL stability condition. Implicit methods offer an alternative, yielding the possibility to choose the time…

Numerical Analysis · Mathematics 2026-03-26 Alessandra Zappa , Matteo Semplice

This paper considers the numerical integration of semilinear evolution PDEs using the high order linearly implicit methods developped in a previous paper in the ODE setting. These methods use a collocation Runge--Kutta method as a basis,…

Numerical Analysis · Mathematics 2023-10-24 Guillaume Dujardin , Ingrid Lacroix-Violet

We propose an implementation of symplectic implicit Runge-Kutta schemes for highly accurate numerical integration of non-stiff Hamiltonian systems based on fixed point iteration. Provided that the computations are done in a given floating…

Numerical Analysis · Mathematics 2017-02-14 Mikel Antoñana , Joseba Makazaga , Ander Murua

First-order stochastic methods are the state-of-the-art in large-scale machine learning optimization owing to efficient per-iteration complexity. Second-order methods, while able to provide faster convergence, have been much less explored…

Machine Learning · Statistics 2017-12-01 Naman Agarwal , Brian Bullins , Elad Hazan
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