Hello,
To analyse a partial differential KdV equation, I suggest trying an explicit time-stepping method, such as the fourth order Runge-Kutta method (RK4). This approach allows you to directly manage the time step size, which could be beneficial for handling nonlinear aspects of the KdV equation. Here's a basic framework for implementing an explicit time-stepping method.
x = linspace(-L/2, L/2, N);
theta = 3*c/2 * sech(sqrt(c)/2 * x).^2;
k2 = kdv(theta + 0.5*dt*k1, dx, N);
k3 = kdv(theta + 0.5*dt*k2, dx, N);
k4 = kdv(theta + dt*k3, dx, N);
theta = theta + (dt/6)*(k1 + 2*k2 + 2*k3 + k4);
axis([-L/2, L/2, -1, 4]);
function dydt = kdv(~, y, dx, N)
D1 = circshift(y, -1) - circshift(y, 1);
D2 = circshift(y, -2) - 2*y + circshift(y, 2);
D3 = circshift(y, -3) - 3*circshift(y, -1) + 3*circshift(y, 1) - circshift(y, 3);
dydt = -6*y.*(D1/(2*dx)) - D3/(2*dx)^3;
This is a simplified example and needs adjustments based on your specific requirements, such as boundary conditions, initial conditions, and the domain size.
Hope this helps!