Note
Go to the end to download the full example code.
2.6 Kuramoto-Sivashinsky - Using PDE class
This example implements a scalar PDE using the PDE. We here
consider the Kuramoto–Sivashinsky equation, which for instance
describes the dynamics of flame fronts:
\[\partial_t u = -\frac12 |\nabla u|^2 - \nabla^2 u - \nabla^4 u\]

0%| | 0/10.0 [00:00<?, ?it/s]
Initializing: 0%| | 0/10.0 [00:00<?, ?it/s]
0%| | 0/10.0 [00:13<?, ?it/s]
0%| | 0.01/10.0 [00:24<6:41:11, 2409.56s/it]
0%| | 0.02/10.0 [00:24<3:20:23, 1204.80s/it]
1%|▏ | 0.13/10.0 [00:24<30:29, 185.36s/it]
65%|██████▍ | 6.47/10.0 [00:24<00:13, 3.73s/it]
65%|██████▍ | 6.47/10.0 [00:24<00:13, 3.73s/it]
100%|██████████| 10.0/10.0 [00:24<00:00, 2.41s/it]
100%|██████████| 10.0/10.0 [00:24<00:00, 2.41s/it]
from pde import PDE, ScalarField, UnitGrid
grid = UnitGrid([32, 32]) # generate grid
state = ScalarField.random_uniform(grid) # generate initial condition
eq = PDE({"u": "-gradient_squared(u) / 2 - laplace(u + laplace(u))"}) # define the pde
result = eq.solve(state, t_range=10, dt=0.01)
result.plot()
Total running time of the script: (0 minutes 24.238 seconds)