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Physics Informed Neural Networks is a numerical method which uses neural networks to approximate solutions of partial differential equations. It has received a lot of attention and is currently used in numerous physical and engineering…

Numerical Analysis · Mathematics 2025-07-10 Dimitrios Gazoulis , Ioannis Gkanis , Charalambos G. Makridakis

We develop a new integration technique allowing one to construct a rich manifold of particular solutions to multidimensional generalizations of classical $C$- and $S$-integrable Partial Differential Equations (PDEs). Generalizations of…

Exactly Solvable and Integrable Systems · Physics 2015-05-18 A. I. Zenchuk

Integrability conditions for Lie systems are related to reduction or transformation processes. We here analyse a geometric method to construct integrability conditions for Riccati equations following these approaches. This approach provides…

Mathematical Physics · Physics 2011-04-07 José F. Cariñena , Javier de Lucas

We discuss various dualities, relating integrable systems and show that these dualities are explained in the framework of Hamiltonian and Poisson reductions. The dualities we study shed some light on the known integrable systems as well as…

High Energy Physics - Theory · Physics 2009-10-31 V. Fock , A. Gorsky , N. Nekrasov , V. Rubtsov

This paper surveys and discusses recent work adapting partial differential equation (PDE) models to discrete structures.

Discrete Mathematics · Computer Science 2015-05-04 Justin Solomon

Whether integrable, partially integrable or nonintegrable, nonlinear partial differential equations (PDEs) can be handled from scratch with essentially the same toolbox, when one looks for analytic solutions in closed form. The basic tool…

Exactly Solvable and Integrable Systems · Physics 2017-10-16 Robert Conte

We represent a version of multidimensional quasilinear partial differential equation (PDE) together with large manifold of particular solutions given in an integral form. The dimensionality of constructed PDE can be arbitrary. We call it…

Exactly Solvable and Integrable Systems · Physics 2015-06-18 A. I. Zenchuk

Integral equations frequently arise in surface science, and in some cases, they must be treated as inverse problems. In our previous work on optical tweezers, atomic force microscopy, and surface force measurement apparatus, we performed…

Soft Condensed Matter · Physics 2026-01-13 Keito Hashidate , Rieko Iwayasu , Takumi Otake , Ken-ichi Amano

We consider delay differential equations with a polynomially distributed delay. We derive an equivalent system of delay differential equations, which includes just two discrete delays. The stability of the equivalent system and its…

Numerical Analysis · Mathematics 2024-09-27 Roland Pulch

Results about existence and uniqueness of solutions of initial value problem for certain types of partial differential equations are recalled as well as iterative scheme and an error estimate for approximate solutions obtained using this…

Numerical Analysis · Mathematics 2016-02-23 Josef Rebenda , Zdeněk Šmarda

We suggest a construction that, given a trajectorial diffeomorphism between two Hamiltonian systems, produces integrals of them. As the main example we treat geodesic equivalence of metrics. We show that the existence of a non-trivially…

Differential Geometry · Mathematics 2016-09-07 Petar J. Topalov , Vladimir S. Matveev

It has been shown that the existence of a Partial Integral Equation (PIE) representation of a Partial Differential Equation (PDE) simplifies many numerical aspects of analysis, simulation, and optimal control. However, the PIE…

Optimization and Control · Mathematics 2024-03-14 Sachin Shivakumar , Amritam Das , Siep Weiland , Matthew Peet

The heights of iterates of the discrete Painleve equations over number fields appear to grow no faster than polynomials while the heights of generic solutions of non-integrable discrete equations grow exponentially. This gives rise to a…

Exactly Solvable and Integrable Systems · Physics 2009-11-11 R. G. Halburd

Understanding sustainability through modeling involves one of the complex and interdisciplinary activities where mathematics plays a key role. We provide arguments favoring the need for developing global models for measuring the status of…

Optimization and Control · Mathematics 2021-06-15 Arni S. R. Srinivasa Rao , Sireesh Saride

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

This paper develops meshless methods for probabilistically describing discretisation error in the numerical solution of partial differential equations. This construction enables the solution of Bayesian inverse problems while accounting for…

Methodology · Statistics 2017-12-20 Jon Cockayne , Chris Oates , Tim Sullivan , Mark Girolami

In this paper, we deal with a new type of differential equations called anticipated backward doubly stochastic differential equations (anticipated BDSDEs). The coefficients of these BDSDEs depend on the future value of the solution $(Y,…

Probability · Mathematics 2013-07-10 Xiaoming Xu

Construction of splitting-step methods and properties of related non-negativity and boundary preserving numerical algorithms for solving stochastic differential equations (SDEs) of Ito-type are discussed. We present convergence proofs for a…

Numerical Analysis · Mathematics 2007-05-23 Esteban Moro , Henri Schurz

The $2n$ dimensional manifold with two mutually commutative operators of differentiation is introduced. Nontrivial multidimensional integrable systems connected with arbitrary graded (semisimple) algebras are constructed. The general…

Mathematical Physics · Physics 2007-05-23 A. N. Leznov

Partial differential equations (PDEs) are among the most universal and parsimonious descriptions of natural physical laws, capturing a rich variety of phenomenology and multi-scale physics in a compact and symbolic representation. This…

Machine Learning · Computer Science 2023-03-31 Steven L. Brunton , J. Nathan Kutz
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