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    Solving Ordinary Differential Equations in Python

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    Author(s)
    Sundnes, Joakim
    Language
    English
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    Abstract
    This open access volume explains the foundations of modern solvers for ordinary differential equations (ODEs). Formulating and solving ODEs is an essential part of mathematical modeling and computational science, and numerous solvers are available in commercial and open source software. However, no single ODE solver is the best choice for every single problem, and choosing the right solver requires fundamental insight into how the solvers work. This book will provide exactly that insight, to enable students and researchers to select the right solver for any ODE problem of interest, or implement their own solvers if needed. The presentation is compact and accessible, and focuses on the large and widely used class of solvers known as Runge-Kutta methods. Explicit and implicit methods are motivated and explained, as well as methods for error control and automatic time step selection, and all the solvers are implemented as a class hierarchy in Python.
    URI
    https://library.oapen.org/handle/20.500.12657/85081
    Keywords
    Ordinary differential equations; Runge-Kutta methods; scientific programming; Python programming; object-oriented programming; difference equations; adaptive time step methods
    DOI
    10.1007/978-3-031-46768-4
    ISBN
    9783031467684, 9783031467677, 9783031467684
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2024
    Imprint
    Springer Nature Switzerland
    Series
    Simula SpringerBriefs on Computing, 15
    Pages
    114
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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