Commit 2ff1ddd3 authored by René Fritze's avatar René Fritze
Browse files

[docs] fix a couple of non-code "builtin" -> "built-in"

parent 70efbb1e
......@@ -99,7 +99,7 @@ parameter values which maximises reduction error.
## The thermalblock demo explained
In the following we will walk through the thermal block demo step by
In the following we will go through the thermal block demo step by
step in an interactive Python shell. We assume that you are familiar
with the reduced basis method and that you know the basics of
[Python](<https://www.python.org>) programming as well as working
......
......@@ -106,7 +106,7 @@ from pymor.basic import *
```
Then we build a 3-by-3 thermalblock problem that we discretize using pyMOR's
{mod}`builtin discretizers <pymor.discretizers.builtin>` (see
{mod}`built-in discretizers <pymor.discretizers.builtin>` (see
{doc}`tutorial_builtin_discretizer` for an introduction to pyMOR's discretization toolkit).
```{code-cell}
......@@ -187,7 +187,7 @@ fom.solution_space
which means that the created {{ VectorArrays }} will internally hold
{{ NumPy_arrays }} for data storage. The number is the dimension of the
vector. We have here a {{ NumpyVectorSpace }} because pyMOR's builtin
vector. We have here a {{ NumpyVectorSpace }} because pyMOR's built-in
discretizations are built around the NumPy/SciPy stack. If `fom` would
represent a {{ Model }} living in an external PDE solver, we would have
a different type of {{ VectorSpace }} which, for instance, might hold a
......
......@@ -107,7 +107,7 @@ Dirichlet boundary conditions
u((x_1, x_2), \mu) = 2x_1\mu + 0.5,\quad x=(x_1, x_2) \in \partial\Omega.
```
We discretize the problem using pyMOR's builtin discretization toolkit as
We discretize the problem using pyMOR's built-in discretization toolkit as
explained in {doc}`tutorial_builtin_discretizer`:
```{code-cell}
......
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