diff --git a/make-pdf.sh b/make-pdf.sh
index 1f88838..b587d4a 100755
--- a/make-pdf.sh
+++ b/make-pdf.sh
@@ -1,13 +1,11 @@
 # source this file with "." in a shell
 
-# note this script assumes the following paths/versions: python3.7 , /Users/thuerey/Library/Python/3.7/bin/jupyter-book
-# updated for nMBA !
-
 # do clean git checkout for changes from json-cleanup-for-pdf.py via:
 # git checkout diffphys-code-burgers.ipynb diffphys-code-ns.ipynb diffphys-code-sol.ipynb physicalloss-code.ipynb bayesian-code.ipynb supervised-airfoils.ipynb reinflearn-code.ipynb physgrad-code.ipynb physgrad-comparison.ipynb physgrad-hig-code.ipynb
 
+
 echo
-echo WARNING - still requires one manual quit of first pdf/latex pass, use shift-x to quit
+echo WARNING - still requires one manual quit of first pdf/latex pass, use shift-x to quit, then fix latex
 echo
 
 PYT=python3
@@ -18,49 +16,27 @@ ${PYT} json-cleanup-for-pdf.py
 # clean / remove _build dir ?
 
 /Users/thuerey/Library/Python/3.9/bin/jupyter-book build .  --builder pdflatex
-xelatex book
 
-exit # sufficient for newer jupyter book versions
+#necessary fixes for jupyter-book 1.0.3 
+    #open book.tex in text editor:
+    #problem 1: replace all 
+        #begin{align} with begin{aligned}
+        #end{align} with end{aligned}
+
+    #problem 2:
+        #\begin{equation*}
+        #\begin{split}
+        #\begin{equation}  <- aligned
+            #...
+        #\end{equation}  <- aligned
+        #\end{split}
+        #\end{equation*}
+
+# manual
+#xelatex book
+#xelatex book
+
+# unused fixup-latex.py
 
 
 
-
-
-
-# old "pre" GEN
-#/Users/thuerey/Library/Python/3.7/bin/jupyter-book build . --builder pdflatex
-#/Users/thuerey/Library/Python/3.9/bin/jupyter-book build . --builder pdflatex
-
-# old cleanup
-
-cd _build/latex
-#mv book.pdf book-xetex.pdf # not necessary, failed anyway
-# this generates book.tex
-
-rm -f book-in.tex sphinxmessages-in.sty book-in.aux book-in.toc
-# rename book.tex -> book-in.tex  (this is the original output!)
-mv book.tex book-in.tex
-mv sphinxmessages.sty sphinxmessages-in.sty
-mv book.aux book-in.aux
-mv book.toc book-in.toc
-#mv sphinxmanual.cls sphinxmanual-in.cls
-
-${PYT} ../../fixup-latex.py
-# reads book-in.tex -> writes book-in2.tex
-
-# remove unicode chars via unix iconv
-# reads book-in2.tex -> writes book.tex
-iconv -c -f utf-8 -t ascii book-in2.tex > book.tex
-
-# finally run pdflatex, now it should work:
-# pdflatex -recorder book
-pdflatex book
-pdflatex book
-
-# for convenience, archive results in main dir
-mv book.pdf ../../pbfl-book-pdflatex.pdf
-tar czvf ../../pbdl-latex-for-arxiv.tar.gz *
-cd ../..
-ls -l ./pbfl-book-pdflatex.pdf ./pbdl-latex-for-arxiv.tar.gz
-
-
diff --git a/others-GANs.md b/others-GANs.md
index 7501776..03a018e 100644
--- a/others-GANs.md
+++ b/others-GANs.md
@@ -163,12 +163,12 @@ This is a highly challenging solution manifold, and requires an extended "cyclic
 that pushes the discriminator to take all the physical parameters under consideration into account.
 Interestingly, the generator learns to produce realistic and accurate solutions despite 
 being trained purely on data, i.e. without explicit help in the form of a differentiable physics solver setup.
+The figure below shows a range of example outputs of a physically-parametrized GAN {cite}`chu2021physgan`. 
 
 ```{figure} resources/others-GANs-meaningful-fig11.jpg
 ---
 name: others-GANs-meaningful-fig11
 ---
-A range of example outputs of a physically-parametrized GAN {cite}`chu2021physgan`. 
 The network can successfully extrapolate to buoyancy settings beyond the
 range of values seen at training time.
 ```
diff --git a/others-intro.md b/others-intro.md
index 4ce1a2b..78dc2e3 100644
--- a/others-intro.md
+++ b/others-intro.md
@@ -1,7 +1,7 @@
 Additional Topics
 =======================
 
-The next sections will give a shorter introduction to other topics that are highly 
+The next sections will give a shorter introduction to other classic topics that are 
 interesting in the context of physics-based deep learning. These topics (for now) do
 not come with executable notebooks, but we will still point to existing open source 
 implementations for each of them.
diff --git a/others-lagrangian.md b/others-lagrangian.md
index 5e48393..be833eb 100644
--- a/others-lagrangian.md
+++ b/others-lagrangian.md
@@ -6,12 +6,13 @@ While this is straight-forward for cases such as data consisting only of integer
 for continuously changing quantities such as the temperature in a room. 
 While the previous examples have focused on aspects beyond discretization
 (and used Cartesian grids as a placeholder), the following chapter will target 
-scenarios where learning with dynamically changing and adaptive discretization has a benefit.
+scenarios where learning Neural operators with dynamically changing 
+and adaptive discretizations have a benefit.
 
 
 ## Types of computational meshes
 
-Generally speaking, we can distinguish three types of computational meshes (or "grids")
+As outlined in {doc}`supervised-arch`, we can distinguish three types of computational meshes (or "grids")
 with which discretizations are typically performed:
 
 - **structured** meshes: Structured meshes have a regular
@@ -85,7 +86,6 @@ for the next stage of convolutions. After expanding
 the size of the latent space over the course of a few layers, it is contracted again 
 to produce the desired result, e.g., an acceleration.
 
-% {cite}`prantl2019tranquil`
 
 ## Continuous convolutions
 
@@ -161,13 +161,14 @@ to reproduce such behavior.
 Nonetheless, an interesting side-effect of having a trained NN for such a liquid simulation
 by construction provides a differentiable solver. Based on a pre-trained network, the learned solver
 then supports optimization via gradient descent, e.g., w.r.t. input parameters such as viscosity.
+The following image shows an examplary _prediction_ task with continuous convolutions from {cite}`ummenhofer2019contconv`.
 
 ```{figure} resources/others-lagrangian-canyon.jpg
 ---
 name: others-lagrangian-canyon
 ---
 An example of a particle-based liquid spreading in a landscape scenario, simulated with 
-learned approach using continuous convolutions {cite}`ummenhofer2019contconv`.
+learned, continuous convolutions.
 ```
 
 ## Source code
diff --git a/others-timeseries.md b/others-timeseries.md
index dce7c49..3d960e7 100644
--- a/others-timeseries.md
+++ b/others-timeseries.md
@@ -126,6 +126,8 @@ Ideally, this step is furthermore unrolled over time to stabilize the evolution
 The resulting training will be significantly more expensive, as more weights need to be trained at once,
 and a much larger number of intermediate states needs to be processed. However, the increased 
 cost typically pays off with a reduced overall inference error.
+The following images show several time frames of an example prediction of {cite}`wiewel2020lsssubdiv`, 
+which additionally couples the learned time evolution with a numerically solved advection step. 
 
 
 ```{figure} resources/others-timeseries-lss-subdiv-prediction.jpg
@@ -133,8 +135,6 @@ cost typically pays off with a reduced overall inference error.
 height: 300px
 name: timeseries-lss-subdiv-prediction
 ---
-Several time frames of an example prediction from {cite}`wiewel2020lsssubdiv`, which additionally couples the
-learned time evolution with a numerically solved advection step. 
 The learned prediction is shown at the top, the reference simulation at the bottom.
 ```