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This paper introduces a novel paradigm for constructing linearly implicit and high-order unconditionally energy-stable schemes for general gradient flows, utilizing the scalar auxiliary variable (SAV) approach and the additive Runge-Kutta…

Numerical Analysis · Mathematics 2023-07-11 Xuelong Gu , Wenjun Cai , Yushun Wang

In this paper we propose and investigate a general approach to constructing local energy-preserving algorithms which can be of arbitrarily high order in time for solving Hamiltonian PDEs. This approach is based on the temporal…

Numerical Analysis · Mathematics 2021-03-31 Yuwen Li , Xinyuan Wu

Gyrocenter dynamics of charged particles plays a fundamental role in plasma physics. In particular, accuracy and conservation of energy are important features for correctly performing long-time simulations. For this purpose, we here propose…

Numerical Analysis · Mathematics 2020-05-26 Luigi Brugnano , Felice Iavernaro , Ruili Zhang

In this work, we develop a class of up to third-order energy-stable schemes for the Cahn--Hilliard equation. Building on Lawson's integrating factor Runge--Kutta method, which is widely used for stiff semilinear equations, we discuss its…

Numerical Analysis · Mathematics 2024-11-26 Haifeng Wang , Jingwei Sun , Hong Zhang , Xu Qian , Songhe Song

The energy dissipation law and the maximum bound principle are two critical physical properties of the Allen--Cahn equations. While many existing time-stepping methods are known to preserve the energy dissipation law, most apply to a…

Numerical Analysis · Mathematics 2024-05-01 Chaoyu Quan , Xiaoming Wang , Pinzhong Zheng , Zhi Zhou

Exponential time differencing methods is a power tool for high-performance numerical simulation of computationally challenging problems in condensed matter physics, fluid dynamics, chemical and biological physics, where mathematical models…

Numerical Analysis · Mathematics 2024-10-15 Evelina V. Permyakova , Denis S. Goldobin

In this paper, we present a comprehensive long-time stability analysis of a second-order explicit exponential Runge--Kutta (ERK2) method for the Cahn--Hilliard (CH) equation. By employing Fourier spectral collocation in space and a…

Numerical Analysis · Mathematics 2025-12-08 Jing Guo

In this note we discuss the construction of high order asymptotic preserving numerical schemes for the Boltzmann equation. The methods are based on the use of Implicit-Explicit (IMEX) Runge-Kutta methods combined with a penalization…

Numerical Analysis · Mathematics 2012-02-24 Giacomo Dimarco , Lorenzo Pareschi

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

In this paper, we present a linearly implicit energy-preserving scheme for the Camassa-Holm equation by using the multiple scalar auxiliary variables approach, which is first developed to construct efficient and robust energy stable schemes…

Numerical Analysis · Mathematics 2020-03-18 Chaolong Jiang , Yuezheng Gong , Wenjun Cai , Yushun Wang

Many important initial value problems have the property that energy is non-increasing in time. Energy stable methods, also referred to as strongly stable methods, guarantee the same property discretely. We investigate requirements for…

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

Using a recent characterization of energy-preserving B-series, we derive the explicit conditions on the coefficients of a Runge-Kutta method that ensure energy preservation (for Hamiltonian systems) up to a given order in the step size,…

Numerical Analysis · Mathematics 2025-01-24 Gabriel A. Barrios de León , David I. Ketcheson , Hendrik Ranocha

We develop arbitrarily high-order, stationarity-preserving stabilized finite element methods for multidimensional nonlinear hyperbolic balance laws on Cartesian grids. We aim at approximating all the steady states of the problem at hand,…

Numerical Analysis · Mathematics 2026-03-25 Moussa Ziggaf , Davide Torlo , Mario Ricchiuto

We propose a new explicit pseudo-energy and momentum conserving scheme for the time integration of Hamiltonian systems. The scheme, which is formally second-order accurate, is based on two key ideas: the integration during the time-steps of…

Numerical Analysis · Mathematics 2020-08-14 Frédéric Marazzato , Alexandre Ern , Christian Mariotti , Laurent Monasse

We present a novel methodology for constructing arbitrarily high-order structure-preserving methods tailored for damped Hamiltonian systems. This method combines the idea of exponential integrator and energy-preserving collocation methods,…

Numerical Analysis · Mathematics 2024-08-14 Lu Li

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 provide a framework for high-order discretizations of nonlinear scalar convection-diffusion equations that satisfy a discrete maximum principle. The resulting schemes can have arbitrarily high order accuracy in time and space, and can be…

Numerical Analysis · Mathematics 2021-09-20 Manuel Quezada de Luna , David I. Ketcheson

Structure-preserving algorithms and algorithms with uniform error bound have constituted two interesting classes of numerical methods. In this paper, we blend these two kinds of methods for solving nonlinear Hamiltonian systems with highly…

Numerical Analysis · Mathematics 2021-02-08 Bin Wang , Yaolin Jiang

This paper aims to develop a linearly implicit structure-preserving numerical scheme for the space fractional sine-Gordon equation, which is based on the newly developed invariant energy quadratization method. First, we reformulate the…

Numerical Analysis · Mathematics 2019-11-19 Yayun Fu , Wenjun Cai , Yushun Wang

This work presents structure-preserving Lift & Learn, a scientific machine learning method that employs lifting variable transformations to learn structure-preserving reduced-order models for nonlinear partial differential equations (PDEs)…

Machine Learning · Computer Science 2026-01-09 Harsh Sharma , Juan Diego Draxl Giannoni , Boris Kramer