Integrators for time stepping

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This page describes how to solve ordinary differential equations numerically with examples from our library.

Introduction and notation

We are solving an initial value problem, given as

\begin{align*} \dot{y}(t) &= f(t, y) \\ y(t_0) &= y_0 \end{align*}

where y is the unknown (possibly vector) function, t_0 is the start time, f is the derivative (the functions we wish to integrate) and y_0 is the initial value of y. Numerically, we usually choose a time step \Delta t and integrate the function up to a certain time t_{\max}. Times os subsequent time steps are denoted with t_i and function values with y_i.

The simplest method is explicit Euler's method: y_{n+1} = y_{n} + \Delta t f(t, y_n)

Explicit (single step) methods

A family of single step methods are exaplicit Runge-Kutta methods

It is given by y_{n+1} = y_n + h \sum_{i=1}^s b_i k_i,

where[1] \begin{align} k_1 & = f(t_n, y_n), \\ k_2 & = f(t_n+c_2h, y_n+h(a_{21}k_1)), \\ k_3 & = f(t_n+c_3h, y_n+h(a_{31}k_1+a_{32}k_2)), \\ & \ \ \vdots \\ k_s & = f(t_n+c_sh, y_n+h(a_{s1}k_1+a_{s2}k_2+\cdots+a_{s,s-1}k_{s-1})). \end{align}

(Note: the above equations have different but equivalent definitions in different texts).[2]
To specify a particular method, one needs to provide the integer s (the number of stages), and the coefficients aij (for 1 ≤ j < is), bi (for i = 1, 2, ..., s) and ci (for i = 2, 3, ..., s). The matrix [aij] is called the Runge–Kutta matrix, while the bi and ci are known as the weights and the nodes.[3] These data are usually arranged in a mnemonic device, known as a Butcher tableau (after John C. Butcher):
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