2.9. Stochastic simulation

This example illustrates how a stochastic simulation can be done.

stochastic simulation
/home/docs/checkouts/readthedocs.org/user_builds/py-pde/checkouts/0.29.0/pde/grids/boundaries/local.py:1822: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def virtual_point(

from pde import KPZInterfacePDE, MemoryStorage, ScalarField, UnitGrid, plot_kymograph

grid = UnitGrid([64])  # generate grid
state = ScalarField.random_harmonic(grid)  # generate initial condition

eq = KPZInterfacePDE(noise=1)  # define the SDE
storage = MemoryStorage()
eq.solve(state, t_range=10, dt=0.01, tracker=storage.tracker(0.5))

Total running time of the script: ( 0 minutes 5.795 seconds)