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We present linearly implicit methods that preserve discrete approximations to local and global energy conservation laws for multi-symplectic PDEs with cubic invariants. The methods are tested on the one-dimensional Korteweg-de Vries…

Numerical Analysis · Mathematics 2020-07-14 Sølve Eidnes , Lu Li

In this paper, we propose a general numerical framework to derive structure-preserving reduced order models for thermodynamically consistent PDEs. Our numerical framework has two primary features: (a) a systematic way to extract reduced…

Numerical Analysis · Mathematics 2023-12-06 Zengyan Zhang , Jia Zhao

In this paper, we introduce a Lagrange multiplier approach to construct linearly implicit energy-preserving schemes of arbitrary order for general Hamiltonian PDEs. Unlike the widely used auxiliary variable methods, this novel approach does…

Numerical Analysis · Mathematics 2026-01-21 Yonghui Bo , Yushun Wang

We propose an Exponential DG approach for numerically solving partial differential equations (PDEs). The idea is to decompose the governing PDE operators into linear (fast dynamics extracted by linearization) and nonlinear (the remaining…

Numerical Analysis · Mathematics 2021-08-11 Shinhoo Kang , Tan Bui-Thanh

We present a novel implicit scheme for the numerical solution of time-dependent conservation laws. The core idea of the presented method is to exploit and approximate the mixed spatial-temporal derivative of the solution that occurs…

Numerical Analysis · Mathematics 2022-12-13 Peter Frolkovič , Michal Žeravý

In the realm of computational fluid dynamics, traditional numerical methods, which heavily rely on discretization, typically necessitate the formulation of partial differential equations (PDEs) in conservative form to accurately capture…

In this work, we propose a novel framework for the numerical solution of time-dependent conservation laws with implicit schemes via primal-dual hybrid gradient methods. We solve an initial value problem (IVP) for the partial differential…

Numerical Analysis · Mathematics 2022-07-18 Siting Liu , Stanley Osher , Wuchen Li , Chi-Wang Shu

In this paper, we present new techniques for solving a large variety of partial differential equations. The proposed method reduces the PDEs to first order differential equations known as classical equations such as Bernoulli, Ricatti and…

Analysis of PDEs · Mathematics 2023-05-19 Noureddine Mhadhbi , Sameh Gana , Hamad Khalid Alharbi

This paper deals with the numerical solution of conservation laws in the two dimensional case using a novel compact implicit time discretization that enables applications of fast algebraic solvers. We present details for the second order…

Numerical Analysis · Mathematics 2025-12-16 Peter Frolkovic , Dagmar Zakova

Partial differential equations (PDEs) are widely used across the physical and computational sciences. Decades of research and engineering went into designing fast iterative solution methods. Existing solvers are general purpose, but may be…

Numerical Analysis · Mathematics 2024-09-23 Jun-Ting Hsieh , Shengjia Zhao , Stephan Eismann , Lucia Mirabella , Stefano Ermon

We consider systems of ordinary differential equations with known first integrals. The notion of a discrete tangent space is introduced as the orthogonal complement of an arbitrary set of discrete gradients. Integrators which exactly…

Numerical Analysis · Mathematics 2015-05-20 Morten Dahlby , Brynjulf Owren , Takaharu Yaguchi

Let $\bbK$ be an ordinary differential field with derivation $\partial$. Let $\cP$ be a system of $n$ linear differential polynomial parametric equations in $n-1$ differential parameters with implicit ideal $\id$. Given a nonzero linear…

Classical Analysis and ODEs · Mathematics 2012-04-10 Sonia L. Rueda

Neural network-based solvers for partial differential equations (PDEs) have attracted considerable attention, yet they often face challenges in accuracy and computational efficiency. In this work, we focus on time-dependent PDEs and observe…

Numerical Analysis · Mathematics 2025-09-30 Guihong Wang , Zheng-An Chen , Tao Luo

We introduce a physics-driven deep latent variable model (PDDLVM) to learn simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric partial differential equations (PDEs). Our formulation…

Machine Learning · Statistics 2023-08-09 Arnaud Vadeboncoeur , Ömer Deniz Akyildiz , Ieva Kazlauskaite , Mark Girolami , Fehmi Cirak

In many applications, the governing PDE to be solved numerically contains a stiff component. When this component is linear, an implicit time stepping method that is unencumbered by stability restrictions is often preferred. On the other…

Numerical Analysis · Mathematics 2021-04-27 Kevin Chow , Steven J. Ruuth

We present a new linearly implicit exponential integrator that preserves the polynomial first integrals or Lyapunov functions for the conservative and dissipative stiff equations, respectively. The method is tested by both oscillated…

Numerical Analysis · Mathematics 2021-11-17 Lu Li

An effective algorithmic method is presented for finding the local conservation laws for partial differential equations with any number of independent and dependent variables. The method does not require the use or existence of a…

Mathematical Physics · Physics 2007-05-23 Stephen C. Anco , George Bluman

In this paper we present an algorithmic approach to the generation of fully conservative difference schemes for linear partial differential equations. The approach is based on enlargement of the equations in their integral conservation law…

Rings and Algebras · Mathematics 2008-04-24 Vladimir P. Gerdt , Yuri A. Blinkov , Vladimir V. Mozzhilkin

A template-based generic programming approach was presented in a previous paper that separates the development effort of programming a physical model from that of computing additional quantities, such as derivatives, needed for embedded…

Mathematical Software · Computer Science 2012-05-18 Roger P. Pawlowski , Eric T. Phipps , Andrew G. Salinger , Steven J. Owen , Christopher M. Siefert , Matthew L. Staten

We consider the construction of semi-implicit linear multistep methods which can be applied to time dependent PDEs where the separation of scales in additive form, typically used in implicit-explicit (IMEX) methods, is not possible. As…

Numerical Analysis · Mathematics 2020-01-14 Giacomo Albi , Lorenzo Pareschi