The Koopman Operator (KO) offers a promising alternative methodology to solve ordinary differential equations analytically. The solution of the dynamical system is analyzed in terms of observables, which are expressed as a linear combination of the eigenfunctions of the system. Coefficients are evaluated via the Galerkin method, using Legendre polynomials as a set of orthogonal basis functions. This tutorial provides a detailed analysis of the Koopman theory, followed by a rigorous explanation of the KO implementation in a computer environment, where a line-by-line description of a MATLAB code solves the Duffing oscillator application.Comment: 22 pages. arXiv admin note: text overlap with arXiv:2110.1211
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Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of ass...
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Dynamical systems have a wide range of applications in mechanics, electrical engineering, chemistry,...
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Ranging from natural phenomena such as biological and chemical systems to artificial technologies su...
The Koopman operator is a linear but infinite-dimensional operator that governs the evolution of sca...
Dynamical systems representing vehicle flight are inherently nonlinear. Currently there are no gener...
Abstract. The Koopman operator is a linear but infinite dimensional opera-tor that governs the evolu...
The Koopman operator provides a linear perspective on non-linear dynamics by focusing on the evoluti...
Recent theoretical developments in dynamical systems and machine learning have allowed researchers t...
The Koopman operator has become an essential tool for data-driven analysis, prediction and control o...
We consider the application of Koopman theory to nonlinear partial differential equations and data-d...
A nonlinear dynamical system can be represented by an infinite-dimensional linear operator known as ...
This paper introduces the Koopman Control Family (KCF), a mathematical framework for modeling genera...
Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of ass...
System representations inspired by the infinite-dimensional Koopman operator (generator) are increas...
Dynamical systems have a wide range of applications in mechanics, electrical engineering, chemistry,...
The Koopman operator is beneficial for analyzing nonlinear and stochastic dynamics; it is linear but...
We present a flexible data-driven method for dynamical system analysis that does not require explici...
Ranging from natural phenomena such as biological and chemical systems to artificial technologies su...
The Koopman operator is a linear but infinite-dimensional operator that governs the evolution of sca...
Dynamical systems representing vehicle flight are inherently nonlinear. Currently there are no gener...
Abstract. The Koopman operator is a linear but infinite dimensional opera-tor that governs the evolu...
The Koopman operator provides a linear perspective on non-linear dynamics by focusing on the evoluti...
Recent theoretical developments in dynamical systems and machine learning have allowed researchers t...
The Koopman operator has become an essential tool for data-driven analysis, prediction and control o...
We consider the application of Koopman theory to nonlinear partial differential equations and data-d...
A nonlinear dynamical system can be represented by an infinite-dimensional linear operator known as ...
This paper introduces the Koopman Control Family (KCF), a mathematical framework for modeling genera...
Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of ass...
System representations inspired by the infinite-dimensional Koopman operator (generator) are increas...