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This paper proposes a new class of mass or energy conservative numerical schemes for the generalized Benjamin-Ono (BO) equation on the whole real line with arbitrarily high-order accuracy in time. The spatial discretization is achieved by…

Numerical Analysis · Mathematics 2021-08-31 Kai Yang

This paper develops the high-order accurate entropy stable finite difference schemes for one- and two-dimensional special relativistic hydrodynamic equations. The schemes are built on the entropy conservative flux and the weighted…

Numerical Analysis · Mathematics 2020-03-30 Junming Duan , Huazhong Tang

A unified theoretical framework is suggested to examine the energy dissipation properties at all stages of additive implicit-explicit Runge-Kutta (IERK) methods up to fourth-order accuracy for gradient flow problems. We construct some…

Numerical Analysis · Mathematics 2024-10-10 Hong-lin Liao , Xuping Wang , Cao Wen

It is well-known that the Allen-Cahn equation not only satisfies the energy dissipation law but also possesses the maximum bound principle (MBP) in the sense that the absolute value of its solution is pointwise bounded for all time by some…

Numerical Analysis · Mathematics 2022-03-15 Lili Ju , Xiao Li , Zhonghua Qiao

The goal of this paper is to develop 2nd order Implicit-Explicit Runge-Kutta (IMEX-RK) finite volume (FV) schemes for solving 1d parabolic PDEs for option pricing, with possible nonlinearities in the source and advection terms. The spatial…

We present a set of linear, second order, unconditionally energy stable schemes for the Allen-Cahn equation with nonlocal constraints that preserves the total volume of each phase in a binary material system. The energy quadratization…

Numerical Analysis · Mathematics 2018-10-15 Xiaobo Jing , Jun Li , Xueping Zhao , Qi Wang

The space nonlocal Allen-Cahn equation is a famous example of fractional reaction-diffusion equations. It is also an extension of the classical Allen-Cahn equation, which is widely used in physics to describe the phenomenon of two-phase…

Numerical Analysis · Mathematics 2025-02-05 Yuxin Zhang , Hengfei Ding

In this paper, we construct stochastic symplectic Runge--Kutta (SSRK) methods of high strong order for Hamiltonian systems with additive noise. By means of colored rooted tree theory, we combine conditions of mean-square order 1.5 and…

Numerical Analysis · Mathematics 2017-05-24 Weien Zhou , Jingjing Zhang , Jialin Hong , Songhe Song

A wide range of physical phenomena exhibit auxiliary admissibility criteria, such as conservation of entropy or various energies, which arise implicitly under the exact solution of their governing PDEs. However, standard temporal schemes,…

Numerical Analysis · Mathematics 2024-01-29 Mohammad R. Najafian , Brian C. Vermeire

In this paper, a novel high-order, mass and energy-conserving scheme is proposed for the regularized logarithmic Schr\"{o}dinger equation(RLogSE). Based on the idea of the supplementary variable method (SVM), we firstly reformulate the…

Numerical Analysis · Mathematics 2024-11-11 Fan Yang , Zhida Zhou , Chaolong Jiang

We construct a family of embedded pairs for optimal strong stability preserving explicit Runge-Kutta methods of order $2 \leq p \leq 4$ to be used to obtain numerical solution of spatially discretized hyperbolic PDEs. In this construction,…

Numerical Analysis · Mathematics 2022-05-17 Sidafa Conde , Imre Fekete , John N. Shadid

It is well-known that a numerical method which is at the same time geometric structure-preserving and physical property-preserving cannot exist in general for Hamiltonian partial differential equations. In this paper, we present a novel…

Numerical Analysis · Mathematics 2019-07-25 Chuchu Chen , Jialin Hong , Chol Sim , Kwang Sonwu

We present a parametric family of semi-implicit second order accurate numerical methods for non-conservative and conservative advection equation for which the numerical solutions can be obtained in a fixed number of forward and backward…

Numerical Analysis · Mathematics 2023-12-01 Peter Frolkovič , Svetlana Krišková , Michaela Rohová , Michal Žeravý

We construct new first- and second-order pressure correction schemes using the scalar auxiliary variable (SAV) approach for the Navier-Stokes equations. These schemes are linear, decoupled and only require a sequence of solving Poisson type…

Numerical Analysis · Mathematics 2020-02-24 Xiaoli Li , Jie Shen , Zhengguang Liu

The scalar auxiliary variable (SAV) approach is a highly efficient method widely used for solving gradient flow systems. This approach offers several advantages, including linearity, unconditional energy stability, and ease of…

Numerical Analysis · Mathematics 2024-07-26 Jinpeng Zhang , Xiaoping Wang

We propose a predictor-corrector adaptive method for the study of hyperbolic partial differential equations (PDEs) under uncertainty. Constructed around the framework of stochastic finite volume (SFV) methods, our approach circumvents…

Numerical Analysis · Mathematics 2024-01-24 Jake J. Harmon , Svetlana Tokareva , Anatoly Zlotnik , Pieter J. Swart

In this work, we study long-time numerical integration of Hamiltonian systems subject to linear perturbations. By introducing an energy-induced metric, we establish a straightforward, coordinate-free criterion for dissipativity that ensures…

Numerical Analysis · Mathematics 2026-03-03 Lu Li , Xiaoli Li , Zaijiu Shang , Quanquan Xu

In this paper, we propose a high-order energy-conserving semi-Lagrangian discontinuous Galerkin(ECSLDG) method for the Vlasov-Ampere system. The method employs a semi-Lagrangian discontinuous Galerkin scheme for spatial discretization of…

Numerical Analysis · Mathematics 2025-04-30 Xiaofeng Cai , Qingtao Li , Hongtao Liu , Haibiao Zheng

In this work, we provide a deep investigation of a family of arbitrary high order numerical methods for hyperbolic partial differential equations (PDEs), with particular emphasis on very high order versions, i.e., with order higher than 5.…

Numerical Analysis · Mathematics 2025-05-09 Lorenzo Micalizzi , Eleuterio F. Toro

Optimizing the learning rate remains a critical challenge in machine learning, essential for achieving model stability and efficient convergence. The Vector Auxiliary Variable (VAV) algorithm introduces a novel energy-based self-adjustable…

Machine Learning · Computer Science 2024-11-12 Jiahao Zhang , Christian Moya , Guang Lin
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