Solving Ordinary Differential Equations in Python: Simula SpringerBriefs on Computing, cartea 15
Autor Joakim Sundnesen Limba Engleză Paperback – 10 noi 2023
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Specificații
ISBN-13: 9783031467677
ISBN-10: 3031467671
Pagini: 114
Ilustrații: XII, 114 p. 22 illus., 17 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.19 kg
Ediția:1st ed. 2024
Editura: Springer Nature Switzerland
Colecția Springer
Seria Simula SpringerBriefs on Computing
Locul publicării:Cham, Switzerland
ISBN-10: 3031467671
Pagini: 114
Ilustrații: XII, 114 p. 22 illus., 17 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.19 kg
Ediția:1st ed. 2024
Editura: Springer Nature Switzerland
Colecția Springer
Seria Simula SpringerBriefs on Computing
Locul publicării:Cham, Switzerland
Cuprins
Preface.- Programming a Simple ODE Solver.- Improving the Accuracy.- Stable Solvers for Stiff ODE Systems.- Adaptive Time Step Methods.- Modeling Infectious Diseases.- Programming of Difference Equations.- References.- Index.
Notă biografică
Joakim Sundnes is Chief Research Scientist at Simula Research Laboratory and teaches undergraduate programming at the University of Oslo. His research interests are scientific computing and computational science, particularly related to biomechanics and computational physiology. Mathematical models in these fields are typically formulated as differential equations, and he has spent more than two decades developing and implementing solvers for these models. He is also responsible for the main introductory programming class for natural science students at the University of Oslo, which includes a thorough introduction to ordinary differential equations.
Textul de pe ultima copertă
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.
Caracteristici
This book is open access, which means that you have free and unlimited access Numerous examples and code segments help the reader to understand important and fundamental concepts The book provides a deep understanding of ODE solvers and software Explains both, the underlying mathematics and the practical implementation in Python