Finish project. Hopefully

report/main.tex

@@ -18,6 +18,10 @@
 \usepackage{float}
 \usepackage{todonotes}
 
+% tikz decorations
+\usepackage{tikz}
+\usetikzlibrary{decorations.pathreplacing}
+
 \usepackage{upgreek} %upalpha in Saxena2021 Reference
 \usepackage[utf8]{inputenc}
 \usepackage{hyperref}
@@ -29,6 +33,7 @@
 
 
 \usepackage{xcolor}
+\definecolor{turq}{RGB}{64,224,208}
 %\newcommand{\contrib}[1]{\textcolor{red}{#1}}
 %\newcommand{\comment}[1]{\textcolor{blue}{#1}}
 
@@ -75,8 +80,7 @@ apsrev41Control%
 
 
 \begin{abstract}
-    Advances in artificial intelligence/machine learning methods provide tools that have broad applicability in scientific research.  These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. 
-    This review provides a snapshot of nuclear physics research  which has been transformed by  artificial intelligence and machine learning techniques. 
+  The basis for many data-driven methods is the optimization of numerical models to describe relations in data. With the increasing availability of large datasets and the growing complexity of models, there is an increasing need for efficient optimization techniques that can handle the computational challenges associated with big data. Furthermore, a good optimization metric can be crucial to find the best possible model to describe the data. In this paper different methods of such optimization are studied in the context of large datasets and big data with possibly noise-affected input data. To render the best possible fits a multitude of cost functions, minimization algorithms and other approaches to reduce the computation associated with the optimization process are studied and evaluated. Reducing the computational cost is also important to improve the economical and ecological footprint of training large models on big data, as the spread of datadriven methods in all areas of life is increasing rapidly.
 \end{abstract}
 
 \maketitle
@@ -91,7 +95,7 @@ apsrev41Control%
 \section{Methods} \label{sec:methods}
 \input{chapters/methods.tex}
 
-\section{Results} \label{sec:results}
+\section{Results and Discussion} \label{sec:results}
 \input{chapters/results.tex}
 
 \section{Conclusion} \label{sec:conclusion}