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Simulation of complex dynamical systems arising in many applications is computationally challenging due to their size and complexity. Model order reduction, machine learning, and other types of surrogate modeling techniques offer cheaper…

Numerical Analysis · Mathematics 2026-02-11 Steven Roberts , Andrey A Popov , Arash Sarshar , Adrian Sandu

We present a high order, robust, and stable shock-capturing technique for finite element approximations of ideal MHD. The method uses continuous Lagrange polynomials in space and explicit Runge-Kutta schemes in time. The shock-capturing…

Numerical Analysis · Mathematics 2021-12-17 Tuan Anh Dao , Murtazo Nazarov

This paper presents a class of Crank-Nicolson (CN) type schemes enhanced by radial basis function (RBF) interpolation for the time integration of linear parabolic partial differential equations (PDEs). The resulting RBF-CN schemes preserve…

Numerical Analysis · Mathematics 2025-09-09 Subhankar Nandi , Satyajit Pramanik

This paper presents two kinds of strategies to construct structure-preserving algorithms with homogeneous Neumann boundary conditions for the sine-Gordon equation, while most existing structure-preserving algorithms are only valid for zero…

Numerical Analysis · Mathematics 2019-09-04 Wenjun Cai , Chaolong Jiang , Yushun Wang

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

We present a general, high-order, fully explicit relaxation scheme which can be applied to any system of nonlinear hyperbolic conservation laws in multiple dimensions. The scheme consists of two steps. In a first (relaxation) step, the…

Numerical Analysis · Mathematics 2016-09-06 Pauline Lafitte , Ward Melis , Giovanni Samaey

An additive Runge-Kutta method is used for the time stepping, which integrates the linear stiff terms by an explicit singly diagonally implicit Runge-Kutta (ESDIRK) method and the nonlinear terms by an explicit Runge-Kutta (ERK) method. In…

Numerical Analysis · Mathematics 2024-05-08 Ke Chen , Daniel Appelö , Tracy Babb , Per-Gunnar Martinsson

In this paper, we investigate linear first- and second-order numerical schemes for the Allen--Cahn equation with a general (possibly degenerate) mobility. Compared with existing numerical methods, our schemes employ a novel dynamic…

Numerical Analysis · Mathematics 2025-10-21 Hongfei Fu , Dianming Hou , Zhonghua Qiao , Bingyin Zhang

Segregated Runge-Kutta (SRK) schemes are time integration methods for the incompressible Navier-Stokes equations. In this approach, convection and diffusion can be independently treated either explicitly or implicitly, which in particular…

Numerical Analysis · Mathematics 2025-06-12 Pavel Bakhvalov

We propose a family of integrators, Flow-Composed Implicit Runge-Kutta (FCIRK) methods, for perturbations of nonlinear ordinary differential equations, consisting of the composition of flows of the unperturbed part alternated with one step…

Numerical Analysis · Mathematics 2017-11-17 Mikel Antoñana , Joseba Makazaga , Ander Murua

This paper is devoted to examining the stability of Runge-Kutta methods for solving nonlinear Volterra delay-integro-differential-algebraic equations (DIDAEs) with constant delay. Hybrid numerical schemes combining Runge-Kutta methods and…

Numerical Analysis · Mathematics 2025-08-19 Gehao Wang , Yuexin Yu

Compact Runge-Kutta (cRK) methods are a class of high order methods for solving hyperbolic conservation laws characterized by their compact stencil including only immediate neighboring finite elements. A Compact Runge-Kutta flux…

Numerical Analysis · Mathematics 2026-04-03 Arpit Babbar , Qifan Chen , Hendrik Ranocha

This work constructs the first-ever sixth-order exponential Runge--Kutta (ExpRK) methods for the time integration of stiff parabolic PDEs. First, we leverage the exponential B-series theory to restate the stiff order conditions for ExpRK…

Numerical Analysis · Mathematics 2024-02-28 Vu Thai Luan , Trky Alhsmy

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

In numerical time-integration with implicit-explicit (IMEX) methods, a within-step adaptable decomposition called residual balanced decomposition is introduced. With this decomposition, the requirement of a small enough residual in the…

Numerical Analysis · Mathematics 2019-02-11 Savio B. Rodrigues

We describe methods for proving bounds on infinite-time averages in differential dynamical systems. The methods rely on the construction of nonnegative polynomials with certain properties, similarly to the way nonlinear stability can be…

Dynamical Systems · Mathematics 2021-06-25 David Goluskin

In distributed model predictive control (DMPC), where a centralized optimization problem is solved in distributed fashion using dual decomposition, it is important to keep the number of iterations in the solution algorithm, i.e. the amount…

Optimization and Control · Mathematics 2013-07-11 Pontus Giselsson , Anders Rantzer

Maximal regularity is a fundamental concept in the theory of partial differential equations. In this paper, we establish a fully discrete version of maximal regularity for a parabolic equation. We derive various stability results in…

Numerical Analysis · Mathematics 2016-02-23 Tomoya Kemmochi , Norikazu Saito

Linearized numerical stability bounds for solving the nonlinear time-dependent Schr\"odinger equation (NLSE) using explicit finite-differencing are shown. The bounds are computed for the fourth-order Runge-Kutta scheme in time and both…

Numerical Analysis · Computer Science 2013-01-03 Ronald M. Caplan , Ricardo Carretero-González

Time-sensitive machine learning benefits from Sequential Probability Ratio Test (SPRT), which provides an optimal stopping time for early classification of time series. However, in finite horizon scenarios, where input lengths are finite,…

Machine Learning · Computer Science 2025-01-31 Akinori F. Ebihara , Taiki Miyagawa , Kazuyuki Sakurai , Hitoshi Imaoka
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