Hello, I would like to model the Cahn Hilliard equation with a diffusion coefficient that depends on the cell variable (concentration X) and time. The diffusion coefficient (Diff) is proportional to the second derivative of the free energy (d2G), for which I have the values in an external file, for a range of concentrations. Initially I interpolated d2G with a polynomial function and could easily express Diff as a function of Xf = X.arithmeticFaceValue

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However, I realised that this interpolation was not appropriate in my case. The only way I found to obtain a correct interpolation of d2G is to use a B-spline interpolation, with scipy.interpolate.UnivariateSpline. The piece of code is as follows (d2G_fit is the array of values that I imported from an external file): X = np.arange(0.011,1.0,0.001) X_var = CellVariable(name=r"$X_{at}$", mesh=mesh, hasOld=True) noise = GaussianNoiseVariable(mesh=mesh, mean=X_mean, variance=1.0e-7).value X_var[:] = noise X_var.updateOld() Xf = X_var.arithmeticFaceValue d2G = UnivariateSpline(X, d2G_fit[id_mean_spino], k=3, s=0) Diff = d2G(Xf) My problem is that when I start looping with: while time < duration: res0 = eq.sweep(X_var, dt=dt, solver=solver) I have the error message: IndexError: diffusion coefficent tensor is not an appropriate shape for this mesh I also tried: Diff = Variable(value=d2G(Xf)) and Diff = d2G(Xf)*Variable(list(d2G(Xf))) but I have the same error. Could you tell me how I could work this out? Additional comment: Diff evolves with time, I update it at selected timestep in the loop. Any hint would be greatly appreciated! Thank you in advance! Clara

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