diff --git a/apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox b/apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox
index 781378aec7d973c9bdcbe970a579b9f4f1a966ce..7e7afaac16df46dc27249ef46bb0d93f50ac2d16 100644
--- a/apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox
+++ b/apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox
@@ -5,7 +5,7 @@ namespace walberla{
 
 \section overview Overview
 
-This tutorial demonstrates how to use <a href="https://pycodegen.pages.i10git.cs.fau.de/lbmpy/notebooks/00_tutorial_lbmpy_walberla_overview.html" target="_blank">lbmpy</a> with waLBerla to generate efficient implementations of various Lattice Boltzmann Methods (LBM) to be included in large-scale distributed memory fluid flow simulations. While waLBerla provides an advanced framework for setting up and running complex fluid simulations, lbmpy brings the flexibility to generate highly optimized LBMs for CPUs and GPUs. Manually writing an efficient C++ or GPU implementation of an advanced LBM can be very cumbersome. Especially because the intensive compute kernel needs to be optimized for specific hardware. For this reason, lbmpy was developed. It is a Python framework which allows defining a set of LB equations at an abstract level, which allows developing on the mathematical description of the problem directly and then generates a highly optimized C, OpenCL or CUDA implementation of these equations. An introduction to lbmpy can be found <a href="https://pycodegen.pages.i10git.cs.fau.de/lbmpy/notebooks/00_tutorial_lbmpy_walberla_overview.html" target="_blank">here</a>.
+This tutorial demonstrates how to use <a href="https://pycodegen.pages.i10git.cs.fau.de/lbmpy/notebooks/00_tutorial_lbmpy_walberla_overview.html" target="_blank">lbmpy</a> with waLBerla to generate efficient implementations of Lattice Boltzmann Methods (LBM) to be included in large-scale distributed memory fluid flow simulations. While waLBerla provides an advanced framework for setting up and running complex fluid simulations, lbmpy brings the flexibility to generate highly optimized LBMs for CPUs and GPUs. Manually writing an efficient C++ or GPU implementation of an advanced LBM can be very cumbersome, especially because the compute kernel needs to be optimized for specific hardware. For this reason, lbmpy was developed. It is a Python framework which allows defining a set of LB equations at an abstract level, thus enabling development on the mathematical description of the problem directly and then generate a highly optimized C, OpenCL or CUDA implementation of these equations. An introduction to lbmpy can be found <a href="https://pycodegen.pages.i10git.cs.fau.de/lbmpy/notebooks/00_tutorial_lbmpy_walberla_overview.html" target="_blank">here</a>.
 
 As in the previous tutorial (\ref tutorial_codegen01), we will first define the numeric methods and set up code generation in a Python script. We will then include the generated code in a waLBerla application to simulate a two-dimensional shear flow in a periodic domain. Additionally, it will be shown how a waLBerla simulation with complex initial conditions can be initialized using parameter files.