The Crank-Nicolson method rewrites a discrete time linear PDE as a matrix multiplication $\phi_{n+1}=C \phi_n$. Nonlinear PDE’s pose some additional problems, but are solvable as well this way (by linearizing every timestep). A major advantage here is that going $k$ steps into the future is just $\phi_{n+k}=C^{k}\phi_n$, and calculating a matrix power is polynomial time. The method is in general very stable.

For an assignment I had to construct the Crank-Nicolson matrix for a simple linear 1 dimension PDE, which had to be derived by hand. That’s a bit labourus, so I made a method to derive it automatically. As I came across it while sifting through some older files, and could still not find that method elsewhere online, I thought I might as well write it up here.

# Derivation (1D)

This is the standard Crank-Nicolson expression for a linear, 1 dimensional PDE. The time stepping matrix would then be $\mathbf{C}=\mathbf{A}^{-1}\mathbf{B}$.

Since $f$ is linear, we can expand it into the set of its impulse responses $\left\{h^k\right\}$. These are the vector outputs $h^k = f\left(\delta^k\right)$ by applying it to a shifted unit impulse $\delta^k_i = \delta_{ik}$. We can then rewrite $f$ in terms of its impulse responses:

The Crank-Nicolson equation can be rewritten by substituting $f$ for this definition with the goal of reformulating it as a matrix equation. Focusing on a single element $\phi^{n+1}_i$ and moving the $\phi^n$ terms not resulting from $f\left(\phi\right)$ into the sum one obtains

Equating this with the definition of the matrix-vector dot product
$(\mathbf{A}\vec x)_i = \sum_k A_{ik} x_k$ gives the elements of the Crank-Nicolson matrices $\mathbf{A}$ and $\mathbf{B}$:

# Implementation

Here is a simple diffusion-advection example in Javascript. Click to reset and apply new values.

Diffusion(2nd derivative):

The implementation in Python is a straightforward translation of the equations above. Note that int(i == k) is the Kronecker delta $\delta_{ik}$.

import numpy as np
import numpy.linalg as LA

def cn_lin_mat(f, n, dt):
d = np.eye(n)                       # shifted impulses {delta^k}
h = [f(di) for di in d]             # shifted impulse responses {h^k}
A = np.zeros((n, n))
B = np.zeros((n, n))
for i in range(n):
for k in range(1, n+1):
k = k % n
A[i, k] = int(i == k) - dt/2*h[k][i]
B[i, k] = int(i == k) + dt/2*h[k][i]
Ainv = LA.inv(A)
C = Ainv @ B
return C


# Higher Dimensions

This generalizes trivially to higher dimensions. You just have to rewrite f so that it works on 1 dimensional data, by reshaping it at appriopiate times. So, for a 2d linear PDE over a field with width W and height H it would result in the following code. You’d probably want to use sparse arrays for $\mathbf{A}$ and $\mathbf{B}$, since they have $(WH)^2$ elements, of which most are 0.

def f1d(phi):
phi = phi.reshape((H, W))
phi_next = f(phi)
return phi_next.flatten()

C = cn_lin_mat(f1d, W*H, dt)
phi1 = (C @ phi0.flatten()).reshape((H, W))