 ### [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]() 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 ` (see {mod}`built-in discretizers ` (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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