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In this note, we present a new numerical method for solving backward stochastic differential equations. Our method can be viewed as an analogue of the classical finite element method solving deterministic partial differential equations.

Probability · Mathematics 2011-06-07 Penghui Wang , Xu Zhang

Although the theory of density evolution in maps and ordinary differential equations is well developed, the situation is far from satisfactory in continuous time systems with delay. This paper reviews some of the work that has been done…

Dynamical Systems · Mathematics 2022-11-07 Michael C. Mackey , Marta Tyran-Kamińska

The fundamental matrix and the delay Lyapunov matrix of linear delay difference equations are introduced. Some properties of the Lyapunov matrix, and the jump discontinuities of its derivative are proven, leading to its construction in the…

Dynamical Systems · Mathematics 2016-12-15 Emanuel Rocha , Sabine Mondié , Michael Di Loreto

In this paper we consider a class of boundary value problems for third order nonlinear functional differential equation. By the reduction of the problem to operator equation we establish the existence and uniqueness of solution and…

Numerical Analysis · Mathematics 2021-01-26 Dang Quang A , Dang Quang Long

For a mixed stochastic differential equation involving standard Brownian motion and an almost surely H\"older continuous process $Z$ with H\"older exponent $\gamma>1/2$, we establish a new result on its unique solvability. We also establish…

Probability · Mathematics 2012-11-13 Yuliya Mishura , Georgiy Shevchenko

In this article, we study about the solutions of second order linear differential equations by considering several conditions on the coefficients of homogenous linear differential equation and its associated non-homogenous linear…

Complex Variables · Mathematics 2023-06-02 Naveen Mehra , Garima Pant , S. K. Chanyal

An efficient approximate version of implicit Taylor methods for initial-value problems of systems of ordinary differential equations (ODEs) is introduced. The approach, based on an approximate formulation of Taylor methods, produces a…

Numerical Analysis · Mathematics 2024-02-05 Antonio Baeza , Raimund Bürger , María del Carmen Martí , Pep Mulet , David Zorío

In in this paper we show how using D.A. it is found a simple change of variables (c.v.) that brings us to obtain differential equations simpler than the original one. In a pedagogical way (at least we try to do that) and in order to make…

Physics Education · Physics 2007-05-23 José Antonio Belinchón

The general theory of (nonlinear) partial differential equations originated by S. Lie had a significant development in the past 30-40 years. Now this theory has solid foundations, a proper language, proper techniques and problems, and a…

Analysis of PDEs · Mathematics 2013-08-28 Alexandre M. Vinogradov

This is an introduction to the theory of disconjugacy for a second order linear differential equation. We give new proofs of some of basic results and obtain new sufficient conditions for disconjugacy (in particular, on the whole real…

Classical Analysis and ODEs · Mathematics 2008-12-01 V. Ya. Derr

There is no general existence theorem for solutions for nonlinear difference equations, so we must prove the existence of solutions in accordance with models one by one. In our work, we found theorems for the existence of analytic solutions…

Classical Analysis and ODEs · Mathematics 2007-05-23 Mami Suzuki

A splitting scheme for backward doubly stochastic differential equations is proposed. The main idea is to decompose a backward doubly stochastic differential equation into a backward stochastic differential equation and a stochastic…

Numerical Analysis · Mathematics 2021-03-17 Feng Bao , Yanzhao Cao , He Zhang

A nonlinear partial differential equation is a nonlinear relationship between an unknown function and how it changes due to two or more input variables. A numerical method reduces such an equation to arithmetic for quick visualization, but…

History and Overview · Mathematics 2019-09-27 R. Corban Harwood

We provide a set of counterexamples for the monotonicity of the Newton-Hewer method for solving the discrete-time algebraic Riccati equation in dynamic settings, drawing a contrast with the Riccati difference equation.

Optimization and Control · Mathematics 2020-11-02 Mohammad Akbari , Bahman Gharesifard , Tamas Linder

The solution to partial differential equations using deep learning approaches has shown promising results for several classes of initial and boundary-value problems. However, their ability to surpass, particularly in terms of accuracy,…

Numerical Analysis · Mathematics 2023-08-23 Ziad Aldirany , Régis Cottereau , Marc Laforest , Serge Prudhomme

Ordinary differential equations of the first order on the torus have been investigated in detail by H. Poincar\'e and A. Denjoy. The long-standing problem of generalising these results for the equations of the order $k>1$ (or for the…

Classical Analysis and ODEs · Mathematics 2024-07-04 Lev Sakhnovich

The class of ordinary linear constant coefficient differential equations is naturally embedded into a wider class by associating differential equations to algebraic curves.

Classical Analysis and ODEs · Mathematics 2016-05-09 Vakhtang Lomadze

The evolution of the methods used to find solutions of Einstein's field equations during the last 100 years is described. Early papers used assumptions on the coordinate forms of the metrics. Since the 1950s more invariant methods have been…

General Relativity and Quantum Cosmology · Physics 2009-11-11 M. A. H. MacCallum

This paper extends the discriminant associated to second order linear constant coefficient differential equations to general second order linear differential equations. The main result of this paper is that the discriminant of a second…

Classical Analysis and ODEs · Mathematics 2016-11-15 Eric Kehoe

Recent years have witnessed a growth in mathematics for deep learning--which seeks a deeper understanding of the concepts of deep learning with mathematics and explores how to make it more robust--and deep learning for mathematics, where…

Machine Learning · Computer Science 2023-10-31 Derick Nganyu Tanyu , Jianfeng Ning , Tom Freudenberg , Nick Heilenkötter , Andreas Rademacher , Uwe Iben , Peter Maass