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In our previous works, we introduced, for each (super)manifold, a commutative algebra of densities. It is endowed with a natural invariant scalar product. In this paper, we study geometry of differential operators of second order on this…

Differential Geometry · Mathematics 2017-07-25 H. M. Khudaverdian , Th. Th. Voronov

An algebraic approach for factorizing nonlinear partial differential equations (PDEs) and systems of PDEs is provided. In the particular case of second order linear and nonlinear PDEs and systems of PDEs, necessary and sufficient conditions…

Analysis of PDEs · Mathematics 2011-07-26 Mahouton Norbert Hounkonnou , Pascal Dkengne Sielenou

This paper, being the sequel of [An inverse problem in Polya-Schur theory. I. Non-genegerate and degenerate operators], studies a class of linear ordinary differential operators with polynomial coefficients called \emph{exactly solvable};…

Dynamical Systems · Mathematics 2024-12-03 Per Alexandersson , Nils Hemmingsson , Boris Shapiro

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

Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling. Mechanism-based computation following PDEs has long been an essential paradigm for studying topics…

Machine Learning · Computer Science 2022-11-21 Shudong Huang , Wentao Feng , Chenwei Tang , Jiancheng Lv

We prove the invariance of homogeneous second-order Hamiltonian operators under the action of projective reciprocal transformations. We establish a correspondence between such operators in dimension $n$ and $3$-forms in dimension $n + 1$.…

Mathematical Physics · Physics 2023-09-06 Pierandrea Vergallo , Raffaele Vitolo

Many processes in science and engineering can be described by partial differential equations (PDEs). Traditionally, PDEs are derived by considering first principles of physics to derive the relations between the involved physical quantities…

Machine Learning · Statistics 2019-03-27 Jens Berg , Kaj Nyström

We introduce a class of second order backward stochastic differential equations and show relations to fully non-linear parabolic PDEs. In particular, we provide a stochastic representation result for solutions of such PDEs and discuss Monte…

Probability · Mathematics 2007-05-23 Patrick Cheridito , H. Mete Soner , Nizar Touzi , Nicolas Victoir

In this paper we investigate the sensitivity analysis of parameterized nonlinear variational inequalities of second kind in a Hilbert space. The challenge of the present work is to take into account a perturbation on all the data of the…

Optimization and Control · Mathematics 2018-02-27 Samir Adly , Loïc Bourdin

In the first part of planned series of papers the formal general solutions to selection of 80 examples of different types of second order nonlinear PDEs in two independent variables with constant parameters are given. The main goal here is…

Mathematical Physics · Physics 2008-01-29 Yu. N. Kosovtsov

We construct, for any given ${\ell}=\frac{1}{2}+{\mathbb{N}}_0$, the second-order, linear PDEs which are invariant under the centrally extended Conformal Galilei Algebra. \par At the given ${\ell}$, two invariant equations in one time and…

Mathematical Physics · Physics 2015-07-01 N. Aizawa , Z. Kuznetsova , F. Toppan

Potential algebras can be used effectively in the analysis of the quantum systems. In the article, we focus on the systems described by a separable, 2x2 matrix Hamiltonian of the first order in derivatives. We find integrals of motion of…

Mathematical Physics · Physics 2015-06-11 Vit Jakubsky

Inverse problems involving partial differential equations (PDEs) can be seen as discovering a mapping from measurement data to unknown quantities, often framed within an operator learning approach. However, existing methods typically rely…

Numerical Analysis · Mathematics 2025-02-10 Sung Woong Cho , Hwijae Son

Recent decades have provided a host of examples and applications motivating the study of nonlocal differential operators. We discuss a class of such operators acting on bounded domains, focusing on those with integrable kernels having…

Analysis of PDEs · Mathematics 2024-08-29 Mikil Foss , Michael Pieper

This paper develops a new framework for designing and analyzing convergent finite difference methods for approximating both classical and viscosity solutions of second order fully nonlinear partial differential equations (PDEs) in 1-D. The…

Numerical Analysis · Mathematics 2013-02-28 Xiaobing Feng , Chiu-Yen Kao , Thomas Lewis

A Lagrangian formalism for variational second-order delay ordinary differential equations (DODEs) is developed. The Noether operator identity for a DODE is established, which relates the invariance of a Lagrangian function with the…

Mathematical Physics · Physics 2023-03-17 V. A. Dorodnitsyn , R. V. Kozlov , S. V. Meleshko

Shape Theory, together with Shape-and-Scale Theory, comprise Relational Theory. This consists of $N$-point models on a manifold $M$, for which some geometrical automorphism group $G$ is regarded as meaningless and is thus quotiented out…

General Relativity and Quantum Cosmology · Physics 2018-10-25 Edward Anderson

Two new approaches to solving first-order quasilinear elliptic systems of PDEs in many dimensions are proposed. The first method is based on an analysis of multimode solutions expressible in terms of Riemann invariants, based on links…

Mathematical Physics · Physics 2014-10-01 A. M. Grundland , V. Lamothe

In this paper, we investigate the fractional powers of block operator matrices, with a particular focus on their applications to partial differential equations (PDEs). We develop a comprehensive theoretical framework for defining and…

Spectral Theory · Mathematics 2025-04-18 Hatem Baloudi , Mohamed Ali Dbeibia , Aref Jeribi

Partial Differential Equations (PDEs) are central to science and engineering. Since solving them is computationally expensive, a lot of effort has been put into approximating their solution operator via both traditional and recently…

Machine Learning · Computer Science 2025-02-14 Alessandro Longhi , Danny Lathouwers , Zoltán Perkó
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