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Mixed precision Runge--Kutta methods have been recently developed and used for the time-evolution of partial differential equations. Two-derivative Runge--Kutta schemes may offer enhanced stability and accuracy properties compared to…

Numerical Analysis · Mathematics 2026-02-17 Sigal Gottlieb , Zachary J. Grant , Cesar Herrera

This work introduces a new class of Runge-Kutta methods for solving nonlinearly partitioned initial value problems. These new methods, named nonlinearly partitioned Runge-Kutta (NPRK), generalize existing additive and component-partitioned…

Numerical Analysis · Mathematics 2025-04-07 Tommaso Buvoli , Ben S. Southworth

In this paper we discuss the use of implicit Runge-Kutta schemes for the time discretization of optimal control problems with evolution equations. The specialty of the considered discretizations is that the discretizations schemes for the…

Numerical Analysis · Mathematics 2013-11-05 Thomas G. Flaig

Motivated by studies on fully discrete numerical schemes for linear hyperbolic conservation laws, we present a framework on analyzing the strong stability of explicit Runge-Kutta (RK) time discretizations for semi-negative autonomous linear…

Numerical Analysis · Mathematics 2018-11-28 Zheng Sun , Chi-Wang Shu

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

Stochastic Hamiltonian partial differential equations, which possess the multi-symplectic conservation law, are an important and fairly large class of systems. The multi-symplectic methods inheriting the geometric features of stochastic…

Numerical Analysis · Mathematics 2022-08-10 Jialin Hong , Baohui Hou , Qiang Li , Liying Sun

In this paper we study arbitrarily high-order energy-conserving methods for simulating the dynamics of a charged particle. They are derived and studied within the framework of Line Integral Methods (LIMs), previously used for defining…

Numerical Analysis · Mathematics 2019-10-17 L. Brugnano , J. I. Montijano , L. Rández

Learning dynamical systems through purely data-driven methods is challenging as they do not learn the underlying conservation laws that enable them to correctly generalize. Existing port-Hamiltonian neural network methods have recently been…

Machine Learning · Computer Science 2026-02-18 Maximino Linares , Guillaume Doras , Thomas Hélie

In this paper, we present a framework to construct general stochastic Runge-Kutta Lawson schemes. We prove that the schemes inherit the consistency and convergence properties of the underlying Runge-Kutta scheme, and confirm this in some…

Numerical Analysis · Mathematics 2021-05-14 Kristian Debrabant , Anne Kværnø , Nicky Cordua Mattsson

We propose efficient numerical methods for nonseparable non-canonical Hamiltonian systems which are explicit, K-symplectic in the extended phase space with long time energy conservation properties. They are based on extending the original…

Numerical Analysis · Mathematics 2023-03-01 Beibei Zhu , Lun Ji , Aiqing Zhu , Yifa Tang

In recent years, the class of energy-conserving methods named Hamiltonian Boundary Value Methods (HBVMs) has been devised for numerically solving Hamiltonian problems. In this short note, we study their natural formulation as…

Numerical Analysis · Mathematics 2019-10-17 Pierluigi Amodio , Luigi Brugnano , Felice Iavernaro

In [Baeza et al., Computers and Fluids, 159, 156--166 (2017)] a new method for the numerical solution of ODEs is presented. This methods can be regarded as an approximate formulation of the Taylor methods and it follows an approach that has…

Numerical Analysis · Mathematics 2018-04-11 Antonio Baeza , Sebastiano Boscarino , Pep Mulet , Giovanni Russo , David Zorío

This work presents a new evolutionary optimization algorithm in theoretical mathematics with important applications in scientific computing. The use of the evolutionary algorithm is justified by the difficulty of the study of the…

Algebraic Geometry · Mathematics 2017-10-31 Ivan Martino , Giuseppe Nicosia

The control of relaxation-type systems of ordinary differential equations is investigated using the Hamilton-Jacobi-Bellman equation. First, we recast the model as a singularly perturbed dynamics which we embed in a family of controlled…

Optimization and Control · Mathematics 2024-04-23 Michael Herty , Hicham Kouhkouh

The aim of this paper is to construct and analyze exponential Runge-Kutta methods for the temporal discretization of a class of semilinear parabolic problems with arbitrary state-dependent delay. First, the well-posedness of the problem is…

Numerical Analysis · Mathematics 2025-09-12 Qiumei Huang , Alexander Ostermann , Gangfan Zhong

For Hamiltonian systems, simulation algorithms that exactly conserve numerical energy or pseudo-energy have seen extensive investigation. Most available methods either require the iterative solution of nonlinear algebraic equations at each…

Numerical Analysis · Mathematics 2022-07-04 Stefan Bilbao , Michele Ducceschi , Fabiana Zama

Full relativistic simulations in three dimensions invariably develop runaway modes that grow exponentially and are accompanied by violations of the Hamiltonian and momentum constraints. Recently, we introduced a numerical method…

General Relativity and Quantum Cosmology · Physics 2015-06-25 Pedro Marronetti

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

Generalized Additive Runge-Kutta schemes have shown to be a suitable tool for solving ordinary differential equations with additively partitioned right-hand sides. This work develops symplectic GARK schemes for additively partitioned…

Numerical Analysis · Mathematics 2023-12-14 Michael Günther , Adrian Sandu , Kevin Schäfers , Antonella Zanna

Runge-Kutta methods are affine equivariant: applying a method before or after an affine change of variables yields the same numerical trajectory. However, for some applications, one would like to perform numerical integration after a…

Numerical Analysis · Mathematics 2026-03-17 Ari Stern , Milo Viviani