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Data-driven modeling of dynamical systems is a crucial area of machine learning. In many scenarios, a thorough understanding of the model's behavior becomes essential for practical applications. For instance, understanding the behavior of a…

Machine Learning · Computer Science 2025-04-14 Krzysztof Kacprzyk , Mihaela van der Schaar

We consider the problem of parameter estimation in dynamic systems described by ordinary differential equations. A review of the existing literature emphasizes the need for deterministic global optimization methods due to the nonconvex…

Optimization and Control · Mathematics 2025-06-16 M. Fernández de Dios , Ángel M. González-Rueda , Julio R. Banga , Julio González-Díaz , David R. Penas

We tersely review a recently introduced technique to identify systems of two nonlinearly-coupled Ordinary Di{\S}erential Equations (ODEs) solvable by algebraic operations; and we report some specifc examples of this kind, namely systems of…

Mathematical Physics · Physics 2020-01-08 Francesco Calogero , Farrin Payandeh

Ordinary differential equations (ODEs) can provide mechanistic models of temporally local changes of processes, where parameters are often informed by external knowledge. While ODEs are popular in systems modeling, they are less established…

Methodology · Statistics 2025-07-10 Maren Hackenberg , Astrid Pechmann , Clemens Kreutz , Janbernd Kirschner , Harald Binder

The data-driven discovery of interpretable models approximating the underlying dynamics of a physical system has gained attraction in the past decade. Current approaches employ pre-specified functional forms or basis functions and often…

Machine Learning · Computer Science 2025-07-30 Rahul Golder , M. M. Faruque Hasan

It has been recently pointed out that dynamical systems depending on future values of the unknowns may be useful in different areas of knowledge. We explore in this context the extension of the concept of order reduction that has been…

Computational Physics · Physics 2007-05-23 J. M. Aguirregabiria

Realizations of stochastic process are often observed temporal data or functional data. There are growing interests in classification of dynamic or functional data. The basic feature of functional data is that the functional data have…

Machine Learning · Statistics 2014-10-28 Lerong Li , Momiao Xiong

A new category of Lie algebras, called generalized Lie algebras, is presented such that classical Lie algebras and Lie-Rinehart algebras are objects of this new category. A new philosophy over generalized Lie algebroids theory is presented…

Differential Geometry · Mathematics 2016-02-09 C. M. Arcus , E. Peyghan

For a dynamical system, we study the set of points $\cal W$ whose orbit approximates any chosen point at certain specified rates. Our basic setting is that of left shift acting on topological Markov chains endowed with a local weak Gibbs…

Dynamical Systems · Mathematics 2016-06-09 María Victoria Melián Pérez

It is shown for a simple ODE that it has many symmetry groups beyond its usual Lie group symmetries, when its generalized solutions are considered within the nowhere dense differential algebra of generalized functions.

Analysis of PDEs · Mathematics 2010-03-01 Elemer E Rosinger

We study the set of T-periodic solutions of a class of T-periodically perturbed Differential-Algebraic Equations with separated variables. Under suitable hypotheses, these equations are equivalent to separated variables ODEs on a manifold.…

Classical Analysis and ODEs · Mathematics 2011-12-23 Luca Bisconti

A categorical theory for the discretization of a large class of dynamical systems with variable coefficients is proposed. It is based on the existence of covariant functors between the Rota category of Galois differential algebras and…

Mathematical Physics · Physics 2015-05-13 Piergiulio Tempesta

We generalize the proof of Karamata's Theorem by the method of approximation by polynomials to the operator case. As a consequence, we offer a simple proof of \emph{uniform dual ergodicity} for a very large class of dynamical systems with…

Dynamical Systems · Mathematics 2014-12-09 Ian Melbourne , Dalia Terhesiu

We establish a consistency result by comparing two independent notions of generalised solutions to a large class of linear hyperbolic first order PDE systems with constant coefficients, showing that they eventually coincide. The first is…

Analysis of PDEs · Mathematics 2018-01-25 Nikos Katzourakis

Ordinary differential equations (ODEs) and ordinary difference systems (O$\Delta$Ss) invariant under the actions of the Lie groups $\mathrm{SL}_x(2)$, $\mathrm{SL}_y(2)$ and $\mathrm{SL}_x(2)\times\mathrm{SL}_y(2)$ of projective…

Mathematical Physics · Physics 2016-01-20 Rutwig Campoamor-Stursberg , Miguel A. Rodríguez , Pavel Winternitz

We introduce a method to estimate the size of the domain of definition of the solutions of a meromorphic vector field on a neighborhood of its pole divisor. The corresponding techniques are, in a certain sense, quantitative versions of some…

Dynamical Systems · Mathematics 2013-12-10 Julio C. Rebelo , Helena Reis

A method based on order completion for solving general equations is presented. In particular, this method can be used for solving large classes of nonlinear systems of PDEs, with possibly associated initial and/or boundary value problems.

General Mathematics · Mathematics 2007-09-28 Elemer E Rosinger

We obtain exact analytic expressions for a class of functions expressed as integrals over the Haar measure of the unitary group in d dimensions. Based on these general mathematical results, we investigate generic dynamical properties of…

Quantum Physics · Physics 2013-04-30 Manuel Gessner , Heinz-Peter Breuer

Definition of generalized normal form for a system of ODEs corresponding to an infinitesimal symplectic or contact transformation near a singular point, with an arbitrary polynomial unperturbed part, and a method of its finding are…

Dynamical Systems · Mathematics 2013-01-15 Arthur S. Vaganyan

In the last decade, the scientific community has devolved its attention to the deployment of data-driven approaches in scientific research to provide accurate and reliable analysis of a plethora of phenomena. Most notably, Physics-informed…

Machine Learning · Computer Science 2023-06-21 Mattia Silvestri , Federico Baldo , Eleonora Misino , Michele Lombardi