| 000 | 03057nam a22005415i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-46768-4 | ||
| 003 | DE-He213 | ||
| 005 | 20240507154521.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 231109s2024 sz | s |||| 0|eng d | ||
| 020 |
_a9783031467684 _9978-3-031-46768-4 |
||
| 024 | 7 |
_a10.1007/978-3-031-46768-4 _2doi |
|
| 050 | 4 | _aQA71-90 | |
| 072 | 7 |
_aPBKS _2bicssc |
|
| 072 | 7 |
_aMAT003000 _2bisacsh |
|
| 072 | 7 |
_aPBKS _2thema |
|
| 082 | 0 | 4 |
_a003.3 _223 |
| 100 | 1 |
_aSundnes, Joakim. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 245 | 1 | 0 |
_aSolving Ordinary Differential Equations in Python _h[electronic resource] / _cby Joakim Sundnes. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2024. |
|
| 300 |
_aXII, 114 p. 22 illus., 17 illus. in color. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aSimula SpringerBriefs on Computing, _x2512-1685 ; _v15 |
|
| 505 | 0 | _aPreface -- 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 | 0 | _aOpen Access | |
| 520 | _aThis 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 |
_aMathematics _xData processing. |
|
| 650 | 0 | _aComputer science. | |
| 650 | 0 | _aMathematics. | |
| 650 | 1 | 4 | _aComputational Science and Engineering. |
| 650 | 2 | 4 | _aComputer Science. |
| 650 | 2 | 4 | _aMathematics. |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031467677 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031467691 |
| 830 | 0 |
_aSimula SpringerBriefs on Computing, _x2512-1685 ; _v15 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-46768-4 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-SXCS | ||
| 912 | _aZDB-2-SOB | ||
| 999 |
_c37468 _d37468 |
||