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Parabolic partial differential equations (PDEs) appear in many disciplines to model the evolution of various mathematical objects, such as probability flows, value functions in control theory, and derivative prices in finance. It is often…

Machine Learning · Computer Science 2024-07-18 Xingzi Xu , Ali Hasan , Jie Ding , Vahid Tarokh

In this paper we introduce and investigate a new kind of functional (including ordinary and evolutionary partial) differential equations. The main goal of this paper is to explore our new philosophy by some examples on functional ODEs and…

Analysis of PDEs · Mathematics 2014-02-14 De-Xing Kong , Cheng Zhang

We study the Lie point symmetries of a general class of partial differential equations (PDE) of second order. An equation from this class naturally defines a second-order symmetric tensor (metric). In the case the PDE is linear on the first…

Analysis of PDEs · Mathematics 2015-06-15 Michael Tsamparlis , Andronikos Paliathanasis

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

Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. In recent years machine learning tools are aspiring to…

Machine Learning · Computer Science 2021-06-11 Sifan Wang , Paris Perdikaris

This book encompasses both traditional and modern methods treating partial differential equation (PDE) of first order and second order. There is a balance in making a selfcontained mathematical text and introducing new subjects. The Lie…

Analysis of PDEs · Mathematics 2010-04-14 A. D. R. Choudary , Saima Parveen , Constantin Varsan

Machine learning methods have been lately used to solve partial differential equations (PDEs) and dynamical systems. These approaches have been developed into a novel research field known as scientific machine learning in which techniques…

Machine Learning · Computer Science 2022-12-12 Junho Choi , Namjung Kim , Youngjoon Hong

General solutions of nonlinear ordinary differential equations (ODEs) are in general difficult to find although powerful integrability techniques exist in the literature for this purpose. It has been shown that in some scalar cases…

Exactly Solvable and Integrable Systems · Physics 2015-06-03 Tamaghna Hazra , V. K. Chandrasekar , R. Gladwin Pradeep , M. Lakshmanan

In this paper, we establish a relation between two seemingly unrelated concepts for solving first-order hyperbolic quasilinear systems of partial differential equations in many dimensions. These concepts are based on a variant of the…

Analysis of PDEs · Mathematics 2024-02-28 Alfred Michel Grundland

In their 1968 paper Fujita and Watanabe considered the issue of uniqueness of the trivial solution of semilinear parabolic equations with respect to the class of bounded, non-negative solutions. In particular they showed that if the…

Analysis of PDEs · Mathematics 2017-05-02 R. Laister , J. C. Robinson , M. Sierzega

The paper concerns classical solution of path-dependent partial differential equations (PPDEs) with coefficients depending on both variables of path and path-valued measure, which are crucial to understanding large-scale mean-field…

Probability · Mathematics 2024-07-26 Shanjian Tang , Huilin Zhang

We study the local classification of higher order Fuchsian linear differential equations under various refinements of the classical notion of the "type of differential equation" introduced by Frobenius. The main source of difficulties is…

Classical Analysis and ODEs · Mathematics 2015-07-28 Shira Tanny , Sergei Yakovenko

The interpretation of numerical methods, such as finite difference methods for differential equations, as point estimators allows for formal statistical quantification of the error due to discretisation in the numerical context. Competing…

Methodology · Statistics 2018-05-23 Junyang Wang , Jon Cockayne , Chris Oates

In this study, a recursive solution technique in conjunction with generalized integrating factors is presented and applied to address first and second order linear differential equations. This approach demonstrates practical utility in…

Mathematical Physics · Physics 2025-03-03 Everardo Rivera-Oliva

In general, the system of $2$nd-order partial differential equations made of the Euler-Lagrange equations of classical field theories are not compatible for singular Lagrangians. This is the so-called second-order problem. The first aim of…

Mathematical Physics · Physics 2022-02-02 David Adame-Carrillo , Jordi Gaset , Narciso Román-Roy

The aim of the work is to construct new polynomial systems, which are solutions to certain functional equations which generalize the second-order differential equations satisfied by the so called classical orthogonal polynomial families of…

Classical Analysis and ODEs · Mathematics 2023-07-31 Edmundo J. Huertas , Alberto Lastra , Víctor Soto-Larrosa

In multi-phase fluid flow, fluid-structure interaction, and other applications, partial differential equations (PDEs) often arise with discontinuous coefficients and singular sources (e.g., Dirac delta functions). These complexities arise…

Numerical Analysis · Mathematics 2019-07-24 Chung-Nan Tzou , Samuel Stechmann

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

We give a new global presentation of our results on the asymptotic behavior of an iteration. This paper brings many improvements and corrections to our previous preprints on the subject. Among the applications, we use new methods to compute…

Dynamical Systems · Mathematics 2012-06-29 Guy Cirier

We propose a new approach to learning the subgrid-scale model when simulating partial differential equations (PDEs) solved by the method of lines and their representation in chaotic ordinary differential equations, based on neural ordinary…

Numerical Analysis · Mathematics 2023-04-14 Shinhoo Kang , Emil M. Constantinescu