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We propose an algebraic method that finds a sequence of functions that exponentially approach the solution of any second-order ordinary differential equation (ODE) with any boundary conditions. We define an extended ODE (eODE) composed of a…

Numerical Analysis · Mathematics 2022-06-07 Pedro L. Garrido

Discrete Differential Equations (DDEs) are functional equations that relate polynomially a power series $F(t,u)$ in $t$ with polynomial coefficients in a "catalytic" variable $u$ and the specializations, say at $u=1$, of $F(t,u)$ and of…

Symbolic Computation · Computer Science 2023-05-01 Alin Bostan , Hadrien Notarantonio , Mohab Safey El Din

In this paper, we consider the initial value problem for some nonlinear second-order ODEs of Duffing type. We study the large time behavior of the solutions to this problem, from both the perspectives of mathematical and numerical analysis.…

Classical Analysis and ODEs · Mathematics 2025-04-03 Yusuke Kunimoto , Ikki Fukuda

In this paper we present a new Eulerian finite element method for the discretization of scalar partial differential equations on evolving surfaces. In this method we use the restriction of standard space-time finite element spaces on a…

Numerical Analysis · Mathematics 2022-12-26 Hauke Sass , Arnold Reusken

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

Real-world phenomena can often be conveniently described by dynamical systems (that is, ODE systems in the state-space form). However, if one observes the state of the system only partially, the observed quantities (outputs) and the inputs…

Symbolic Computation · Computer Science 2022-05-17 Dmitrii Pavlov , Gleb Pogudin

Differential equations (DE) constrained optimization plays a critical role in numerous scientific and engineering fields, including energy systems, aerospace engineering, ecology, and finance, where optimal configurations or control…

Machine Learning · Computer Science 2024-10-03 Vincenzo Di Vito , Mostafa Mohammadian , Kyri Baker , Ferdinando Fioretto

We apply the direct method of obtaining reductions to the Toda hierarchy of equations. The resulting equations form a hierarchy of ordinary differential difference equations, also known as delay-differential equations. Such a hierarchy…

Exactly Solvable and Integrable Systems · Physics 2010-05-06 Nalini Joshi , Paul E. Spicer

Multirate behavior of ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) is characterized by widely separated time constants in different components of the solution or different additive terms of the…

Numerical Analysis · Mathematics 2020-01-09 Andreas Bartel , Michael Günther

The combination of Monte Carlo methods and deep learning has recently led to efficient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as variational formulations…

Machine Learning · Computer Science 2022-08-08 Lorenz Richter , Julius Berner

We propose a unified framework for delay differential equations (DDEs) based on deep neural networks (DNNs) - the neural delay differential equations (NDDEs), aimed at solving the forward and inverse problems of delay differential…

Machine Learning · Computer Science 2024-08-27 Housen Wang , Yuxing Chen , Sirong Cao , Xiaoli Wang , Qiang Liu

In the paper we offer a functional-discrete method for solving the Cauchy problem for the first order ordinary differential equations (ODEs). This method (FD-method) is in some sense similar to the Adomian Decomposition Method. But it is…

Numerical Analysis · Mathematics 2010-09-02 Volodymyr Makarov , Denis Dragunov

This work investigates the application of the Newton's method for the numerical solution of a nonlinear boundary value problem formulated through an ordinary differential equation (ODE). Nonlinear ODEs arise in various mathematical modeling…

We develop a new symbolic-numeric algorithm for the certification of singular isolated points, using their associated local ring structure and certified numerical computations. An improvement of an existing method to compute inverse systems…

Symbolic Computation · Computer Science 2011-01-18 Angelos Mantzaflaris , Bernard Mourrain

Carleman linearization is a mathematical technique that transforms nonlinear dynamical systems into infinite-dimensional linear systems, enabling simplified analysis. Initially developed for ordinary differential equations (ODEs) and later…

Optimization and Control · Mathematics 2025-09-03 Marcos A. Hernandez-Ortega , C. M. Rergis , A. Roman-Messina , Erlan R. Murillo-Aguirre

The paper studies a method for solving elliptic partial differential equations posed on hypersurfaces in $\mathbb{R}^N$, $N=2,3$. The method builds upon the formulation introduced in Bertalmio et al., J. Comput. Phys., 174 (2001),…

Numerical Analysis · Mathematics 2013-04-08 Alexey Y. Chernyshenko , Maxim A. Olshanskii

A spectral decomposition method is used to obtain solutions to a class of nonlinear differential equations. We extend this approach to the analysis of the fractional form of these equations and demonstrate the method by applying it to the…

Mathematical Physics · Physics 2015-08-14 Malgorzata Turalska , Bruce J. West

In mathematical physics, the space-fractional diffusion equations are of particular interest in the studies of physical phenomena modelled by L\'{e}vy processes, which are sometimes called super-diffusion equations. In this article, we…

Numerical Analysis · Mathematics 2018-01-03 X. G. Zhu , Z. B. Yuan , F. Liu , Y. F. Nie

The cutoff method, which cuts off the values of a function less than a given number, is studied for the numerical computation of nonnegative solutions of parabolic partial differential equations. A convergence analysis is given for a broad…

Numerical Analysis · Mathematics 2015-06-05 Changna Lu , Weizhang Huang , Erik S. Van Vleck

A state-of-the-art deep domain decomposition method (D3M) based on the variational principle is proposed for partial differential equations (PDEs). The solution of PDEs can be formulated as the solution of a constrained optimization…

Machine Learning · Computer Science 2020-04-03 Ke Li , Kejun Tang , Tianfan Wu , Qifeng Liao
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