Fehlberg¶
(Source code
, png
)
- class Fehlberg(*args)¶
Adaptive order Fehlberg method.
- Parameters:
- transitionFunction
Function
The function defining the flow of the ordinary differential equation. Must have one parameter.
- localPrecisionfloat
The expected absolute error on one step.
- orderint,
The order of the method, ie the exponent in the estimate of the local error for a step of size written as .
- transitionFunction
See also
Notes
The Fehlberg method of order is a one-step explicit method made of two embedded Runge Kutta methods of order and . More precisely, such a method approximate the solution of:
at a given set of locations by first building an approximation over an adapted grid with a number of points not necessarily equal to the number of locations and internal nodes not necessarily part of the set of locations. Then, the solution is approximated by a smooth piecewise polynomial function using
PiecewiseHermiteEvaluation
, which is evaluated over the set of locations.The method proceeds as follows. Knowing the solution at location and a current time step , two approximations and of are built, such that:
where we assume that:
The evolution operators and are constructed as follows:
with . The most desirable property of these methods is their embedded nature: the high-order approximation reuses all the evaluations of needed by the low-order approximation. The coefficients , , and fully specify the method.
For we have:
0
0
0
1
1/2
1
1
1
1/2
For we have:
0
0
0
1/256
1/512
1
1/2
1/2
255/256
255/256
2
1
1/256
255/256
1/512
For we have:
0
0
0
214/891
533/2106
1
1/4
1/4
1/33
0
2
27/40
-189/800
214/891
650/891
800/1053
3
1
729/800
1/35
650/891
-1/78
For the coefficients can be found eg in the C++ source code. For additional theory on these methods see [stoer1993], chapter 7.
Examples
>>> import openturns as ot >>> f = ot.SymbolicFunction(['t', 'y0', 'y1'], ['t - y0', 'y1 + t^2']) >>> phi = ot.ParametricFunction(f, [0], [0.0]) >>> solver = ot.Fehlberg(phi) >>> Y0 = [1.0, -1.0] >>> nt = 100 >>> timeGrid = [(i**2.0) / (nt - 1.0)**2.0 for i in range(nt)] >>> result = solver.solve(Y0, timeGrid)
Methods
Accessor to the object's name.
getName
()Accessor to the object's name.
Transition function accessor.
hasName
()Test if the object is named.
setName
(name)Accessor to the object's name.
setTransitionFunction
(transitionFunction)Transition function accessor.
solve
(*args)Solve ODE.
- __init__(*args)¶
- getClassName()¶
Accessor to the object’s name.
- Returns:
- class_namestr
The object class name (object.__class__.__name__).
- getName()¶
Accessor to the object’s name.
- Returns:
- namestr
The name of the object.
- getTransitionFunction()¶
Transition function accessor.
- Returns:
- transitionFunction
FieldFunction
Transition function.
- transitionFunction
- hasName()¶
Test if the object is named.
- Returns:
- hasNamebool
True if the name is not empty.
- setName(name)¶
Accessor to the object’s name.
- Parameters:
- namestr
The name of the object.
- setTransitionFunction(transitionFunction)¶
Transition function accessor.
- Parameters:
- transitionFunction
FieldFunction
Transition function.
- transitionFunction