fd1d_predator_prey, an Octave code which implements the finite difference method (FDM) for a predator-prey system with time and 1D spatial dependence, by Marcus Garvie.
The code is mostly self explanatory, with the names of variables and parameters corresponding to the symbols used in the finite difference methods described in the paper.
The code employs the sparse matrix facilities when solving
the linear systems, which provides advantages in both matrix storage
and computation time. The code is
The linear systems are solved using the built in function lu.m. We remark that a pure C or Fortran code is likely to be faster than our codes, but with the disadvantage of much greater complexity and length.
The user is prompted for all the necessary parameters, time and space-steps, and initial data. Vector indices cannot be equal to zero; thus the nodal indices 0,...,J are shifted up one unit to give 1,...,(J+1) so xi=(i-1)*h + a.
The program is structured as follows:
The initial data functions are entered by the user as a string, which can take several different formats. Functions are evaluated on an element by element basis, where x=(x1,...,xJ+1) is a vector of grid points, and so a "." must precede each arithmetic operation between vectors. The exception to this rule is when applying intrinsic functions where there is no ambiguity. Some arbitrary examples with an acceptable format include the following:
>> Enter initial prey function u0(x) 0.2*exp(-(x-100).^2)
>> Enter initial predator function v0(x) 0.4*x./(1+x)
or,
>> Enter initial prey function u0(x) 0.3+(x-1200).*(x-2800)
>> Enter initial predator function v0(x) 0.4
This last example shows that for a constant solution vector we need
only enter a single number. It is also possible to enter functions
that are piecewise defined by utilizing the logical operators
&, ('AND'), |, ('OR'), and ~ (`NOT'), applied to
matrices. For example, on a domain Omega=[0,200], to choose
an initial prey density that is equal to 0.4 for
90<=xi<=110, and equal to 0.1 otherwise, the user inputs:
>> Enter initial prey function u0(x) 0.4*((x>90)&(x<110))+0.1*((x<=90)|(x>=110))
The information on this web page is distributed under the MIT license.
fd1d_predator_prey is available in a Fortran90 version and a MATLAB version and an Octave version.
fd_predator_prey, an Octave code which solves a pair of predator prey ode's using a finite difference approximation.
fd1d_advection_ftcs, an Octave code which applies the finite difference method to solve the time-dependent advection equation ut = - c * ux in one spatial dimension, with a constant velocity, using the ftcs method, forward time difference, centered space difference.
fd1d_burgers_lax, an Octave code which applies the finite difference method and the lax-wendroff method to solve the non-viscous time-dependent burgers equation in one spatial dimension.
fd1d_burgers_leap, an Octave code which applies the finite difference method and the leapfrog approach to solve the non-viscous time-dependent burgers equation in one spatial dimension.
fd1d_heat_explicit, an Octave code which uses the finite difference method and explicit time stepping to solve the time dependent heat equation in 1d.
fd1d_heat_implicit, an Octave code which uses the finite difference method and implicit time stepping to solve the time dependent heat equation in 1d.
fd1d_heat_steady, an Octave code which uses the finite difference method to solve the steady (time independent) heat equation in 1d.
fd1d_predator_prey_plot, an Octave code which displays the solution components computed by fd1d.
fd1d_wave, an Octave code which applies the finite difference method to solve the time-dependent wave equation in one spatial dimension.
fd2d_predator_prey, an Octave code which implements a finite difference algorithm for a predator-prey system with spatial variation in 2d.
Marcus Garvie