Dear all,

I apologize for my question sent once again too early - the requested 
functions have of course already been implemented 
(see https://github.com/dealii/dealii/pull/12603 for anyone interested). 

I am currently trying to use this to implement a DG solver for the 
steady-state Porous medium diffusion equation 
(https://en.wikipedia.org/wiki/Porous_medium_equation) and will be happy to 
share the code if I succeed.

Thank you very much for your understanding and best regards,
Nik
On Tuesday, August 5, 2025 at 4:18:13 AM UTC-7 Nik Markus Leuenberger wrote:

> Dear all,
>
> I have a question regarding an *extension* of step.74. 
>
> Eventually, I would like to solve a non-linear Poisson equation using DG 
> elements. I would also like to use automatic differentiation to obtain the 
> Jacobian from the Residual as in step-72. 
>
> To start, I wanted to solve the linear problem as if it was non-linear 
> using a single Newton iteration.
>
> For the cell_worker and boundary_worker, I was able to use calls like the 
> following 
>
> std::vector<ADNumberType> old_solution_values(fe_fv.n_quadrature_points);
> fe_fv[u_fe].get_function_values_from_local_dof_values(dof_values_ad, 
> old_solution_values);
>
> std::vector<Tensor<1, dim, ADNumberType>> old_solution_gradients(fe_fv.
> n_quadrature_points);
> fe_fv[u_fe].get_function_gradients_from_local_dof_values(dof_values_ad, 
> old_solution_gradients);
>
> to then assemble the residual and automatically compute the Jacobian. The 
> full code is appended.
>
> For the face_worker, however, I think I would need functions like 
> std::vector<Tensor<1, dim, ADNumberType>> old_average_of_gradients(fe_fv.
> n_quadrature_points);
> fe_iv[u_fe].get_average_of_gradients_from_local_dof_values(dof_values_ad, 
> old_average_of_gradients);
>
> Since I didn't find these functions, I think they might not exist yet.
>
> Therefore my question. 
> - If these functions don't exist, what alternative approach do you see to 
> get the desired average of gradients of the old solution vector?
>
> Thank you very much in advance for any help and best regards,
> Nik
>

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