Hello, I have been scratching my head trying to find this through the tutorials but can’t seem to find it. I essentially have a functionspace
, which as far as I understood represents a mesh with its corresponding Lagrange polynomials. I am trying to obtain evaluations of it on points inside the domain with certain coefficients that I want to impose.
I have included a short code block of my code. Essentially I am trying to approximate the continuous Dirichlet-to Neumman operator by using an eigendecomposition of a discretized DtN operator.
def compute_eigenfunctions(self):
eigenvalues, eigenvectors = self.eigendecomposition()
V = fem.functionspace(self.domain, ("Lagrange", self.order))
eigenfunctions = []
for i in range(eigenvectors.shape[1]):
eigenvector = eigenvectors[i, :]
eigenfunction = fem.Function(V, x = eigenvector)
eigenfunctions.append(eigenfunction)
return eigenfunctions
I tried this by inspecting the source code for fem.Function
but the x
argument doesn’t seem to be in the right format as an np.ndarray.
Thanks!