國立高雄大學圖資館 |

Language: English

Back

Solving ordinary differential equations in Python

Record Type:	Electronic resources : Monograph/item
Title/Author:	Solving ordinary differential equations in Pythonby Joakim Sundnes.
Author:	Sundnes, Joakim.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xii, 114 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Differential equations.
Online resource:	https://doi.org/10.1007/978-3-031-46768-4
ISBN:	9783031467684$q(electronic bk.)

Solving ordinary differential equations in Python
Sundnes, Joakim.

Solving ordinary differential equations in Python[electronic resource] /by Joakim Sundnes. - Cham :Springer Nature Switzerland :2024. - xii, 114 p. :ill. (chiefly col.), digital ;24 cm. - Simula SpringerBriefs on computing,v. 152512-1685 ;. - Simula SpringerBriefs on computing ;v.1..

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.

Open access.

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.

ISBN: 9783031467684$q(electronic bk.)

Standard No.: 10.1007/978-3-031-46768-4doiSubjects--Topical Terms:

183925
Differential equations.

LC Class. No.: QA371

Dewey Class. No.: 515.35

Solving ordinary differential equations in Python
LDR:02265nmm a2200349 a 4500 001 660434
003 DE-He213
005 20231109145643.0
006 m d
007 cr nn 008maaau
008 241106s2024 sz s 0 eng d
020 $a 9783031467684$q(electronic bk.)
020 $a 9783031467677$q(paper)
024 7 $a 10.1007/978-3-031-46768-4 $2 doi
035 $a 978-3-031-46768-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA371
072 7 $a PBKS $2 bicssc
072 7 $a COM014000 $2 bisacsh
072 7 $a PBKS $2 thema
082 0 4 $a 515.35 $2 23
090 $a QA371 $b .S958 2024
100 1 $a Sundnes, Joakim. $3 884643
245 1 0 $a Solving ordinary differential equations in Python $h [electronic resource] / $c by Joakim Sundnes.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xii, 114 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Simula SpringerBriefs on computing, $x 2512-1685 ; $v v. 15
505 0 $a 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.
506 $a Open access.
520 $a 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.
650 0 $a Differential equations. $3 183925
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Computational Science and Engineering. $3 274685
650 2 4 $a Computer Science. $3 212513
650 2 4 $a Mathematics. $3 184409
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Simula SpringerBriefs on computing ; $v v.1. $3 741740
856 4 0 $u https://doi.org/10.1007/978-3-031-46768-4
950 $a Computer Science (SpringerNature-11645)