000			a
999			_c33769 _d33769
008			250306b xxu||||| |||| 00| 0 eng d
020			_a9783031467677
082			_a003.3 _bSUN
100			_aSundnes, Joakim
245			_aSolving ordinary differential equations in Python
260			_bSpringer, _c2024 _aCham :
300			_axii, 114 p. ; _bill., _c24 cm
365			_b39.99 _c$ _d89.70
490			_aSimula SpringerBriefs on computing, _v2512-1685 ; v.15
504			_aIncludes bibliographical references and index.
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			_aDifferential equations
650			_aA-stable
650			_aBackward Euler method
650			_aDifference equations
650			_aHodgkin-Huxley model
650			_aImplicit RK methods;
650			_aLogistic growth
650			_aMidpoint method
650			_aNewton's method
650			_aODE systems
650			_aOrdinary Differential Equations
650			_aRunge-Kutta methods
650			_aTaylor series
942			_2ddc _cBK