ode45-cash-karp

Integrate a system of Ordinary Differential Equations using the Fifth Order Adaptive Cash-Karp method

Introduction

This module integrates a system of ordinary differential equations of the form

where is a vector of length . Given time step , the Cash-Karp method uses a fifth order Runge-Kutta scheme with a fourth order embedded estimator in order to control the error. In other words, the same intermediate values used in calculating the fifth order update can be used to calculate a fourth order estimate. The difference yields an error estimate, and the error estimate controls the timestep .

Install

$ npm install ode45-cash-karp

Example

Van der Pol oscillator

var ode45 = require('ode45-cash-karp')

// The derivative function for a Van der Pol oscillator:
var vanderpol = function(dydt, y, t) {
  dydt[0] = y[1]
  dydt[1] = 4 * (1-y[0]*y[0])*y[1] - y[0]
}

// Initialize:
var y0 = [2,0],
    t0 = 0,
    dt0 = 1e-3,
    integrator = ode45( y0, vanderpol, t0, dt0 )

// Integrate up to tmax:
var tmax = 10, t = [], y = []
while( integrator.step( tmax ) ) {
  // Store the solution at this timestep:
  t.push( integrator.t )
  y.push( integrator.y )
}

Common patterns

A single adaptive step:

integrator.step()
integrator.y      // current state
integrator.t      // current time
integrator.dt     // newly adapted timestep

A single adaptive step, returning false if reached:

integrator.step( 1.5 ) // returns true if t < 1.5
integrator.step( 1.5 ) // returns false if t = 1.5

Ten adaptive timesteps in sequence:

integrator.steps( 10 )

Ten timesteps, halting early and returning false if is reached::

integrator.steps( 10, 1.5 )

Take any number of timesteps until :

integrator.steps( Infinity, 1.5 )

API

require('ode45-cash-karp')( y0, deriv, t0, dt0 [, options] )

Arguments:

• y0: an array or typed array containing initial conditions. This vector is updated in-place with each integrator step.
• deriv: a function that calculates the derivative. Format is function( dydt, y, t ). Inputs are current state y and current time t, output is the calculated derivative dydt.
• t0: initial time .
• dt0: initial time step .
• options: an optional associative array of options. Valid parameters are:
  • tol: The target error level to be acheived. Default is: .
  • maxIncreaseFactor: The maximum factor by which to increase the timestep if the error tolerance is met. Default value is 10. This limit is applied at the end of a successful timestep.
  • maxDecreaseFactor: The maximum factor by which to decrease the timestep if the error tolerance is not met. Default value is 10. This limit is applied on each trial step until the error tolerance is acheived.
  • dtMinMag: The minimum allowed magnitude of . If limit is exceeded during adaptation, a warning is printed to the console and the timestep completes with clipped to the the prescribed magnitude. If undefined, this limit is ignored. Default value is undefined.
  • dtMaxMag: The maximum allowed magnitude of . This limit is applied at the beginning of each step. If a timestep larger than this magnitude is requested, the timestep is executed with clipped to the prescribed magnitude. If undefined, this limit is ignored. Default value is undefined.
  • errorScaleFunction: The function used to compute a normalizing factor for the error in a given dimension. See below for details.
  • errorReduceFunction: The reduce operation by which errors in each dimension are combined into a single error metric. See below for details.
  • errorPostFunction: An operation applied to the total error. For example, if using the norm this would be a square root. See below for details.
  • verbose: Log covergence warnings. Default is true.

Returns:

Initialized integrator object.

Properties:

• n: dimension of y0.
• y: current state; a reference to input array y0.
• deriv: function that calculates the derivative.
• t: current time, incremented by dt on each time step.
• dt: current time step . Initialized from input dt0. May be changed, but will be overwritten with each adaptive step in order to acheive the prescribed error bound.
• all options are copied to properties on the integrator object and may be changed at any time.

Methods:

• .step( [tLimit] ): takes a single step of the integrator and stores the result in-place in the y property. Returns true if tLimit was not provided or if t has not reached the limit, otherwise returns false, meaning t has reached tLimit.
• .steps( n, [tLimit] ): takes n steps of the integrator, storing the result in-place in the y property. Exits early if tLimit is reached. Returns true if tLimit was not provided or if t has not reached the limit, otherwise returns false, meaning t has reached tLimit. Note that, for example, .steps( Infinity, 10 ) is valid and will take whatever number of step is required to reach .

Error Estimation

Ideally, there would be no choices in error computation since this library would implement the best possible choices, but I've left this configurable.

errorScaleFunction: function( i, dt, y, dydt )

This function receives the dimension number i, the current timestep dt, the current state y, and the derivative calculated at the beginning of the step, dydt. It must return a normalization factor by which the error in the given dimension is normalized. It is executed once at the beginning of each timestep and not for subsequent trial steps. By default, it is:

function errorScaleFunction( i, dt, y, dydt ) {
  return Math.abs(y) + Math.abs(dt * dydt) + 1e-32
}

errorReduceFunction: function( i, accumulatedError, errorEstimate )

This function performs a reduce operation on the per-dimension error. accumulatedError is initially zero. The function must add the error estimate in dimension i and return a new error estimate. By default, the error reduce function simply returns the maximum error:

function errorReduceFunction( i, accumulatedError, errorEstimate ) {
  return Math.max( accumulatedError, Math.abs(errorEstimate))
}

errorPostFunction: function( accumulatedError, errorEstimate )

This function applies a mapping to the total reduced error resulting from errorReduceFunction. For the norm, this would just be Math.sqrt; for the norm, this is simply a no-op:

function errorPostFunction( accumulatedError ) {
  return accumulatedError
}

Credits

(c) 2015 Ricky Reusser. MIT License