Parametric system of ODEs #201
Description
I have a system with one input (t), two outputs (x and y) and two parameters (a and w):
def ode_system(x, y, t):
return [diff(x,t)-(a*x - w*y - x*(x**2 + y**2)), diff(y,t)-(a*y + w*x - y*(x**2 + y**2))]
Currently, I have to train a network every time I want to change the value of the parameters. Is there a way to parameterize the NN in such a way that it can predict the solution for multiple values of the parameters at the same time?
Thank you!