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This work presents a brief discussion and a plan towards the analytical solving of Partial Differential Equations (PDEs) using symbolic computing, as well as an implementation of part of this plan as the PDEtools software-package of…

General Relativity and Quantum Cosmology · Physics 2016-03-23 E. S. Cheb-Terrab , K. von Bulow

The non-commutative strategy developed by Bagarello (see Int. Jour. of Theoretical Physics, 43, issue 12 (2004), p. 2371 - 2394) for the analysis of systems of ordinary differential equations (ODEs) is extended to a class of partial…

Mathematical Physics · Physics 2015-08-25 Jean Ghislain Compaore , Villevo Adanhounme , Mahouton Norbert Hounkonnou

A formalism of arithmetic partial differential equations (PDEs) is being developed in which one considers several arithmetic differentiations at one fixed prime. In this theory solutions can be defined in algebraically closed p-adic fields.…

Number Theory · Mathematics 2021-04-01 Alexandru Buium , Lance Edward Miller

Before we proposed an algebraic technics for the Hamiltonian approach to the evolution systems of partial differential equations, including systems with constraints. Here we further develop this approach and present the defining system of…

Mathematical Physics · Physics 2018-03-13 Victor Zharinov

In this paper, we consider the use of discrete gradients for differential-algebraic equations (DAEs) with a conservation/dissipation law. As one of the most popular numerical methods for conservative/dissipative ordinary differential…

Numerical Analysis · Mathematics 2018-05-15 Shun Sato

We exhibit an adjunction between a category of abstract algebras of partial functions and a category of set quotients. The algebras are those atomic algebras representable as a collection of partial functions closed under relative…

Logic · Mathematics 2022-06-15 Célia Borlido , Brett McLean

INTRODUCTION This papers deals with partial differential equations of second order, linear, with constant and not constant coefficients, in two variables, which admit real characteristics. I face the study of PDEs with the mentality of the…

General Mathematics · Mathematics 2017-11-06 Andrea Pezzi

Partial differential equations with discrete (concentrated) state-dependent delays are studied. The existence and uniqueness of solutions with initial data from a wider linear space is proven first and then a subset of the space of…

Analysis of PDEs · Mathematics 2010-11-11 Alexander V. Rezounenko , Petr Zagalak

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

The Adomian decomposition method is a semi-analytical method for solving ordinary and partial nonlinear differential equations. The aim of this paper is to apply Adomian decomposition method to obtain approximate solutions of nonlinear…

Numerical Analysis · Mathematics 2017-12-27 Iqra Javed , Ashfaq Ahmad , Muzammil Hussain , S. Iqbal

We consider Hamiltonian PDEs that can be split into a linear unbounded operator and a regular non linear part. We consider abstract splitting methods associated with this decomposition where no discretization in space is made. We prove a…

Numerical Analysis · Mathematics 2008-11-26 Erwan Faou , Benoit Grebert , Eric Paturel

A delayed term in a differential equation reflects the fact that information takes significant time to travel from one place to another within a process being studied. Despite de apparent similarity with ordinary differential equations,…

Dynamical Systems · Mathematics 2023-08-24 Gregory Kozyreff

A simple pseudo-Hamiltonian formulation is proposed for the linear inhomogeneous systems of ODEs. In contrast to the usual Hamiltonian mechanics, our approach is based on the use of non-stationary Poisson brackets, i.e. corresponding…

Quantum Physics · Physics 2009-11-11 V. G. Kupriyanov , S. L. Lyakhovich , A. A. Sharapov

We investigate the geometric structure of adjoint systems associated with evolutionary partial differential equations at the fully continuous, semi-discrete, and fully discrete levels and the relations between these levels. We show that the…

Optimization and Control · Mathematics 2025-04-10 Brian K. Tran , Ben S. Southworth , Melvin Leok

When learning dynamical systems from data, embedding physical structure can constrain the solution space and improve generalization, but many physics-informed models assume access to the full system state. This limits their use in partially…

Machine Learning · Computer Science 2026-05-25 Sunniva Meltzer , Sølve Eidnes , Alexander Johannes Stasik

Many equations that model fluid behaviour are derived from systems that encompass multiple physical forces. When the equations are written in non dimensional form appropriate to the physics of the situation, the resulting partial…

Analysis of PDEs · Mathematics 2020-11-18 Susan Friedlander , Anthony Suen

This article is focused on two related topics within the study of partial differential equations (PDEs) that illustrate a beautiful connection between dynamics, topology, and analysis: stability and spatial dynamics. The first is a property…

Dynamical Systems · Mathematics 2019-10-18 Margaret Beck

Many time-dependent linear partial differential equations of mathematical physics and continuum mechanics can be phrased in the form of an abstract evolutionary system defined on a Hilbert space. In this paper we discuss a general framework…

Analysis of PDEs · Mathematics 2019-05-09 Stefan Neukamm , Mario Varga , Marcus Waurick

Motivated by recent work in this area we expand on a generalization of port-Hamiltonian systems that is obtained by replacing the Hamiltonian function representing energy storage by a general Lagrangian subspace. This leads to a new class…

Optimization and Control · Mathematics 2018-08-07 Arjan van der Schaft , Bernhard Maschke

In this paper we establish a connection between non-convex optimization methods for training deep neural networks and nonlinear partial differential equations (PDEs). Relaxation techniques arising in statistical physics which have already…

Machine Learning · Computer Science 2017-06-05 Pratik Chaudhari , Adam Oberman , Stanley Osher , Stefano Soatto , Guillaume Carlier