From a1288d3ffd51bb418f1c04c343235eb33fbae6b3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dennis=20Gl=C3=A4ser?= <dennis.glaeser@iws.uni-stuttgart.de>
Date: Sat, 1 Apr 2023 20:35:19 +0200
Subject: [PATCH] [slides][model] minor changes

---
 slides/model.md | 67 +++++++++++++++++++++----------------------------
 1 file changed, 29 insertions(+), 38 deletions(-)

diff --git a/slides/model.md b/slides/model.md
index 6c2a4c57..2123761c 100644
--- a/slides/model.md
+++ b/slides/model.md
@@ -1,8 +1,8 @@
 ---
-title: Models in DuMuX
+title: Implementing a Model in DuMuX
 ---
 
-# Implementing a new DuMu<sup>X</sup> model
+# What is a DuMu<sup>X</sup> model
 
 ## What is a DuMu<sup>X</sup> model
 A DuMu<sup>X</sup> model is an implementation of a discretized **mathematical model**, generally given by partial differential equations.
@@ -33,15 +33,15 @@ $\begin{equation*}
 * $F_{B,\sigma}$: flux term over sub control volume face (scvf)
 * $q$ source term
 
-How to implement these terms in DuMu<sup>X</sup>?
+Where to implement these terms in DuMu<sup>X</sup>?
 
-**local residual**
+`LocalResidual`
 
-# Local Residual
+# `LocalResidual`
 
-## Local Residual
+## `LocalResidual`
 
-Implements these terms within
+Implements terms of a PDE in the functions
 
 * `computeStorage(...)`
 * `computeFlux(...)`
@@ -57,12 +57,10 @@ $\begin{equation}
 
 with
 
-$\begin{aligned}
-    c: &\:\text{concentration} \\
-    D: &\:\text{constant diffusion coefficient} \\
-    \Omega: &\:\text{spatial domain} \\
-    T: &\:\text{end time}
-\end{aligned}$
+- $c$: concentration
+- $D$: constant diffusion coefficient
+- $\Omega$: spatial domain
+- $T$: end time
 
 ## Example: Diffusion equation
 Discrete model using the Box discretization:
@@ -73,12 +71,10 @@ $\begin{equation}
 
 with
 
-$\begin{aligned}
-    c_B^n: &\:\text{concentration at time $t_n$ and control volume $B$} \\
-    c^n_h: &\:\text{global discrete solution at time $t_n$, interpolated using \textbf{basis functions}} \\
-    \mathbf{n}: &\:\text{unit outer normal vector} \\
-    \sigma: &\:\text{sub control volume face (scvf)} \\
-\end{aligned}$
+- $c_B^n$: concentration at time $t_n$ and control volume $B$
+- $c^n_h$: global discrete solution at time $t_n$, interpolated using __basis functions__
+- $\mathbf{n}$: unit outer normal vector
+- $\sigma$: sub control volume face (scvf)
 
 ## Example: Diffusion equation
 Discrete model using the Box discretization:
@@ -89,7 +85,7 @@ $\begin{equation}
 
 <img src=img/box_scv_scvf.png width="90%">
 
-## Local residual
+## `LocalResidual`
 The local residual of the diffusion model:
 ```cpp
 template<class TypeTag>
@@ -122,11 +118,9 @@ F_{B,\sigma} = -D \nabla c_h^{n+1} \cdot \boldsymbol{n}_{B,\sigma} \vert \sigma
 
 with
 
-$\begin{aligned}
-    c^n_h: &\:\text{global discrete solution at time $t_n$, interpolated using \textbf{basis functions}} \\
-    \mathbf{n}: &\:\text{unit outer normal vector} \\
-    \sigma: &\:\text{sub control volume face (scvf)} \\
-\end{aligned}$
+- $c^n_h$: global discrete solution at time $t_n$, interpolated using __basis functions__
+- $\mathbf{n}$: unit outer normal vector
+- $\sigma$: sub control volume face (scvf)
 
 ## Flux term
 ```cpp
@@ -168,17 +162,19 @@ NumEqVector computeFlux(...) const
     NumEqVector flux;
 
     // Compute the flux
-    flux[Indices::massBalanceEqIdx] = -1.0*vtmv(
-        scvf.unitOuterNormal(), problem.diffusionCoefficient(), gradConcentration
-    )*scvf.area();
+    flux[Indices::massBalanceEqIdx] = -1.0*scvf.area()*vtmv(
+        scvf.unitOuterNormal(),
+        problem.diffusionCoefficient(),
+        gradConcentration
+    );
 
     return flux;
 }
 ```
 
-## Local Residual
+## `LocalResidual`
 
-A **local residual** implements the discretized mathematical model.
+A `LocalResidual` implements the discretized mathematical model.
 
 For its implementation different model-specific properties have to be set
 
@@ -236,18 +232,13 @@ i.e. `TTag::DiffusionModel`
 # Exercise: Model
 
 ## Exercise: Model
-Implementation of a **nonlinear diffusion model**
+Implementation of a **nonlinear diffusion model** for denoising of an MRI image
 
-<figure>
-    <center>
-        <img src="../exercises/extradoc/exercisemodel_mri_denoise.gif" alt="denoising"/>
-        <figcaption> Denosing of MRI image using nonlinear diffusion model.</figcaption>
-    </center>
-</figure>
+<img src="../exercises/extradoc/exercisemodel_mri_denoise.gif" alt="denoising" width="300"/>
 
 ## Tasks
 
 - Implement local residual
 - Set model properties
 - Use model in test case
-- Customize volume variables
\ No newline at end of file
+- Customize volume variables
-- 
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