If you want to use symbolic calculations, you could also leverage SymEngine, 
and use Functions::SymbolicFunction

https://www.dealii.org/current/doxygen/deal.II/classFunctions_1_1SymbolicFunction.html

In your code, you could create such object by making a 

std::unique_ptr<Functions::SymbolicFunction<dim>> fun;

parse its expression from file, and then 

fun = std::make_unique<Functions::SymbolicFunction<dim>>(expression);

In this way the generated function would compute the gradient symbolically.

L.

> On 9 Aug 2021, at 16:39, Marco Feder <marco.fede...@gmail.com> wrote:
> 
> You're right, I totally missed the default parameter h in the constructor.
> 
> Thanks,
> Marco
> Il giorno domenica 8 agosto 2021 alle 22:26:01 UTC+2 Timo Heister ha scritto:
> FunctionParser uses a finite difference approximation for the gradient. I 
> think this is explained in the class documentation.
> This explains the results you see...
> 
> On Sun, Aug 8, 2021, 15:15 Marco Feder <marco....@gmail.com> wrote:
> Ciao Luca!
> 
> > Can you check if you get the same results with this order?
> Yes, the rates are the same. 
> 
> > Are you calling `error_from_exact` with mapping as a first argument?
> Yes
> 
> Actually, I've fixed the issue after reading this:
> > If the Solution<dim> class implements the Gradient, then 
> > ParsedConvergenceTable should use that. 
> 
> Indeed, I realised I wrote:
> error_table.error_from_exact(mapping, dof_handler, solution, exact_solution);
> 
> where exact_solution is a FunctionParser<dim> which will be initialised with 
> the expression of the analytical solution, so it doesn't give any info about 
> the Gradient. Instead, if I replace exact_solution in the last argument with 
> the "classical" Solution<dim>() (so that there's also an implementation of 
> Gradient) I have my expected rates. Apparently it wasn't using the Gradient 
> function. 
> 
> However, looking at the source code and in the documentation of 
> integrate_difference, I don't see how the H^1 norm was computed without 
> having an implementation of the Gradient. I would expect to see an exception 
> asking for its implementation. Was it using some FD-like approximation or am 
> I missing something?
> 
> 
> Thanks,
> Marco
> 
> 
> Il giorno domenica 8 agosto 2021 alle 18:52:03 UTC+2 luca....@gmail.com ha 
> scritto:
> The other option is that you are not calling the `error_from_exact` function 
> that takes a mapping argument, but the one without it, and your boundary 
> cells are introducing some error due to a worse mapping…. Are you calling 
> `error_from_exact` with mapping as a first argument?
> 
> Luca
> 
>> Il giorno 8 ago 2021, alle ore 6:49 PM, Luca Heltai <luca....@gmail.com> ha 
>> scritto:
>> 
>> 
>> Ciao Marco,
>> 
>>> I was expanding step-12 using a manufactured solution to check the order p 
>>> in H^1 (and p+1 in L^2) norm on a uniformly refined mesh for the DG upwind 
>>> method. I've used the ParsedConvergenceTable with a parameter file as 
>>> described in the docs. As Rate_key I'm using the DoFs, while as Rate_mode I 
>>> have reduction_rate_log2. 
>>> 
>>> With p=1 and p=2 everything is fine. But if I set the finite element degree 
>>> to 3, then the H^1 convergence rate decreases, as you can see in the 
>>> attached image.
>>> <Screenshot 2021-08-07 at 17.56.06.png>
>> 
>> Is it possible that you are using different quadrature rules in the two 
>> cases? Your image shows a deterioration of the error on the order of 1e-8 
>> for H1, and 1e-12 for L2, which is very close to machine precision.
>> 
>> Internally the parsed convergence table does the exact same thing you wrote 
>> explicitly. (If you check the source code, you’ll see that it calls 
>> integrate_difference and compute_global_error for each error type you 
>> specify in the parameter file).
>> 
>>> This, however, doesn't happen if I use a classical ConvergenceTable. 
>>> Namely, I first compute the local error in each cell, and then the global 
>>> error in the classical way:
>>> VectorTools::integrate_difference(mapping,dof_handler,solution, 
>>> Solution<dim>(),H1_error_per_cell, 
>>> QGauss<dim>(fe->tensor_degree()+1),VectorTools::H1_norm);
>>> 
>>> const double H1_error = VectorTools::compute_global_error(triangulation, 
>>> H1_error_per_cell,  VectorTools::H1_norm); //assuming I provided also the 
>>> gradient method for the Solution<dim> class
>>> 
>>> 
>>> 
>>> Does anyone have any idea why this is happening? My guess was that while 
>>> computing the H^1 semi-norm the ParsedConvergenceTable class does some 
>>> approximation to compute the gradient from the exact solution expression 
>>> and hence that could be the source of the issue. Conversely, in the 
>>> "ConvergenceTable" way I do define explicitly the gradient of the exact 
>>> solution in the Solution<dim> class.
>> 
>> If the Solution<dim> class implements the Gradient, then 
>> ParsedConvergenceTable should use that. 
>> 
>> You are calling “error_from_exact” of that class, right?
>> 
>> This is where it is called:
>> 
>> https://www.dealii.org/current/doxygen/deal.II/parsed__convergence__table_8h_source.html
>> 
>> Line 621. As you see, the quadrature rule used is a Gauss formula of order 
>> (dh.get_fe().degree+1)*2.
>> 
>> Can you check if you get the same results with this order?
>> 
>> L.
> 
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