## Implement vector valued unknowns by extending the indexing functions

For higher order finite elements or e.g. DG1 discretizations, **we need vector values unknowns** on the discretized domain.

Currently this is implemented (but not tested and most likely not functional!) by template parameters (`ValueType`

) that could theoretically be set to `std::array`

or similar.

As an alternative, **we could extend the indexing functions by another dimension** that refers to the nth element of such a vector valued unknown.

Example: To get the three DG1 unknowns at position x, y on a macro-face an indexing function call could look like this:

```
real_t dg1_a = data[ index< Level >( x, y, DG_CENTER, 0 ) ];
real_t dg1_b = data[ index< Level >( x, y, DG_CENTER, 1 ) ];
real_t dg1_c = data[ index< Level >( x, y, DG_CENTER, 2 ) ];
```

(Such mappings of course must be documented (which would be true for the templated approach as well).)

The function memory is simply extended to the required size.

Further advantages:

- the
`ValueType`

template parameter~~can be removed~~would be restricted to either`float`

or`double`

- the memory layout (AoS vs SoA) can be easily switched by exchanging the indexing function(!) - this would not be possible with the
`ValueType`

-template approach

Disadvantages:

- we would restrict the function classes to unknowns of type
`real_t`

^N`float`

^N or`double`

^N (for`uint_t`

typed functions we then need extra classes (but maybe that's not that bad anyway?)) ~~https://i10git.cs.fau.de/terraneo/tinyhhg/issues/34 would be really hard to solve (but uncertain if we ever need that)~~