From 33f8b45ac88077a3d000bb611823d35b83cf4a6d Mon Sep 17 00:00:00 2001
From: Frederik Hennig <frederik.hennig@fau.de>
Date: Tue, 15 Sep 2020 22:35:31 +0200
Subject: [PATCH] Minor fixes

---
 apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox b/apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox
index 781378aec..7e7afaac1 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.
 
-- 
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