>
> ...which you compute via quadrature? Or do you compute a vector F that 
> corresponds to f(u) somehow? 
>
> If you go the route via vectors you have to pay attention to *what kind 
> of vector you have*, namely one that does or does not incorporate 
> constraints. Dealing with dual space vectors (e.g., rhs vectors) is 
> tricky because they obey different rules than primal space vectors 
> (e.g., solution vectors). 
>
 
I'm assembling a vector F that represent f(u); the action of f(u) on a 
vector field v with FE expansion coefficients V can be represented as F*V. 
Of course if you want to compute the L2 inner product of say, u and v, that 
would be U^*MV where M is the mass matrix. I actually made a wrapper class 
with an extra template parameter to keep track of whether a field is in H^1 
or in (H^1)^* in order to keep myself from making this mistake. Do you 
think there might be some additional subtlety regarding either boundary of 
hanging node constraints in this case?
 

> So u_a, v_a are the same as u, v in the pointwise sense, just 
> interpolated from a uniform mesh to an adaptively refined one obtained 
> from the uniform one? 
>

Not quite, I defined a dealii::TensorFunction object representing the exact 
solution and a perturbation of it that matches it along the boundary; u, v 
are interpolated from the TensorFunction object to the uniform mesh, u_a 
and v_a are interpolated from it to the non-uniform mesh.
 

> Are you using the L2 projection? For example, for the Laplace equation, 
> I think you can only guarantee that the H1 seminorm error is smaller for 
> a finer mesh, but not necessarily the L2 error. It all depends on your 
> equation. 
>
>
I did not know that! I shouldn't have said projection, really I 
interpolated an analytic formula for the solution.
 

> And how do the two meshes differ? 
>

The domain is a rectangle; the grid spacing is half as large in the right 
half of the mesh. 

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