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The operator of double differentiation on a finite interval with Robin boundary conditions perturbed by the composition of a Volterra convolution operator and the differentiation one is considered. We study the inverse problem of recovering…

Spectral Theory · Mathematics 2020-01-28 S. A. Buterin , A. E. Choque Rivero

By constructing the commutative operators chain, we derive the integrable conditions for solving the eigenfunctions of Dirac equation and Schr\"odinger equation. These commutative relations correspond to the intrinsic symmetry of the…

General Physics · Physics 2017-06-02 Ying-Qiu Gu

In this short note, we review several one-dimensional problems such as those involving linear Schroedinger equation, variable-coefficient Helmholtz equation, Zakharov-Shabat system and Kubelka-Munk equations. We show that they all can be…

Mathematical Physics · Physics 2022-03-30 Dmitry Ponomarev

In this paper, we propose a trigonometric-interpolation approach for solutions of second order nonlinear ODEs with mixed boundary conditions. The method interpolates secondary derivative $y''$ of a target solution $y$ by a trigonometric…

Numerical Analysis · Mathematics 2025-04-29 Xiaorong Zou

In this paper, we present a method to identify integrable complex nonlinear oscillator systems and construct their solutions. For this purpose, we introduce two types of nonlocal transformations which relate specific classes of nonlinear…

Exactly Solvable and Integrable Systems · Physics 2013-09-13 R. Mohanasubha , Jane H. Sheeba , V. K. Chandrasekar , M. Senthilvelan , M. Lakshmanan

A third order self-adjoint differential operator with periodic boundary conditions and an one-dimensional perturbation has been considered. For this operator, we first show that the spectrum consists of simple eigenvalues and finitely many…

Classical Analysis and ODEs · Mathematics 2022-12-21 Yixuan Liu , Jun Yan

Integration of nonlinear partial differential equations with the help of the non-commutative integration over octonions is studied. An apparatus permitting to take into account symmetry properties of PDOs is developed. For this purpose…

Analysis of PDEs · Mathematics 2018-12-18 Emmanuel Frenod , Sergey Victor Ludkowski

In this work, the Dirac-type integro di{\S}erential system with one classical boundary condition and another nonlocal integral boundary condition is considered. We obtain the asymptotic formulae for the solutions, eigenvalues and nodal…

Spectral Theory · Mathematics 2022-03-25 Baki Keskin

We suggest a modification of the operator exponential method for the numerical solving the difference linear initial boundary value problems. The scheme is based on the representation of the difference operator for given boundary conditions…

Numerical Analysis · Mathematics 2025-10-20 I. M. Nefedov , I. A. Shereshevski\uı

Olver and Rosenau studied group-invariant solutions of (generally nonlinear) partial differential equations through the imposition of a side condition. We apply a similar idea to the special case of finite-dimensional Hamiltonian systems,…

Mathematical Physics · Physics 2012-11-27 Philip Broadbridge , Claudia M. Chanu , Willard Miller

We study an inverse problem for the fractional Schr\"odinger equation (FSE) with a local perturbation by a linear partial differential operator (PDO) of order smaller than the order of the fractional Laplacian. We show that one can uniquely…

Analysis of PDEs · Mathematics 2022-03-09 Giovanni Covi , Keijo Mönkkönen , Jesse Railo , Gunther Uhlmann

The aim of the paper is firstly to study domains of definitions in terms of boundary conditions of minimal and maximal operators, as well as selfadjoint extensions of a minimal operator associated with the fourth-order differential operator…

Functional Analysis · Mathematics 2022-03-31 Nigar Aslanova , Kh. Aslanov

A key appeal of the recently proposed Neural Ordinary Differential Equation (ODE) framework is that it seems to provide a continuous-time extension of discrete residual neural networks. As we show herein, though, trained Neural ODE models…

Machine Learning · Computer Science 2023-09-12 Katharina Ott , Prateek Katiyar , Philipp Hennig , Michael Tiemann

Neural ordinary differential equations (NODEs) are an effective approach for data-driven modeling of dynamical systems arising from simulations and experiments. One of the major shortcomings of NODEs, especially when coupled with explicit…

Numerical Analysis · Mathematics 2025-12-30 Allen Alvarez Loya , Daniel A. Serino , J. W. Burby , Qi Tang

We present a novel approach that redefines the traditional interpretation of explicit and implicit discretization methods for solving a general class of advection-diffusion equations (ADEs) featuring nonlinear advection, diffusion…

Analysis of PDEs · Mathematics 2025-06-10 Amin Jafarimoghaddam , Manuel Soler , Irene Ortiz

In this short note we discuss ordinary differential equations which linearize upon one (or more) differentiations. Although the subject is fairly elementary, equations of this type arise naturally in the context of integrable systems.

Exactly Solvable and Integrable Systems · Physics 2015-06-26 E. V. Ferapontov , S. R. Svirshchevskii

A new numerical method for solving a scalar ordinary differential equation with a given initial condition is introduced. The method is using a numerical integration procedure for an equivalent integral equation and is called in this paper…

Numerical Analysis · Mathematics 2011-09-06 Alexander Lozovskiy

In this paper, we study the direct and inverse spectral problems for the Schrodinger operator with two generalized Regge boundary conditions. For the direct problem, we give the properties of the spectrum, including the asymptotic…

Spectral Theory · Mathematics 2025-08-22 Xiao-Chuan Xu , Yu-Ting Huang

Neural Ordinary Differential Equations (ODEs) represent a significant advancement at the intersection of machine learning and dynamical systems, offering a continuous-time analog to discrete neural networks. Despite their promise, deploying…

Numerical Analysis · Mathematics 2025-06-18 Matteo Caldana , Jan S. Hesthaven

Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical…

Machine Learning · Computer Science 2023-09-01 Marc Finzi , Andres Potapczynski , Matthew Choptuik , Andrew Gordon Wilson
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