105 lines
2.7 KiB
TeX
105 lines
2.7 KiB
TeX
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\documentclass[aps,rmp,reprint,amsmath,amssymb,graphicx,longbibliography]{revtex4-1}
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\usepackage{bm}
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\usepackage{graphicx}
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\usepackage{upgreek} %upalpha in Saxena2021 Reference
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\hypersetup{breaklinks=true,colorlinks=true,linkcolor=blue,citecolor=blue,filecolor=magenta,urlcolor=blue}
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%\newcommand{\contrib}[1]{\textcolor{red}{#1}}
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\newcommand{\E}{\mathbb{E}}
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\newcommand{\N}{N_\mathrm{train}}
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\newcommand{\Nt}{N_{\mathrm{test}}}
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\makeatletter
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\def\@bibdataout@aps{%
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\immediate\write\@bibdataout{%
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@CONTROL{%
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apsrev41Control%
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\longbibliography@sw{%
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,author="08",editor="1",pages="1",title="0",year="1"%
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}{%
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,author="08",editor="1",pages="1",title="",year="1"%
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}%
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}%
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}%
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\if@filesw \immediate \write \@auxout {\string \citation {apsrev41Control}}\fi
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}
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%\usepackage[left]{lineno}
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%\linenumbers
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\begin{document}
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%\pagenumbering{gobble}
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\title{Regression Analysis and Resampling Methods}
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\author{Lars Bogner}
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\affiliation{\url{https://github.uio.no/larsbog/FYSSTK-Project1}}
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\begin{abstract}
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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.
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This review provides a snapshot of nuclear physics research which has been transformed by artificial intelligence and machine learning techniques.
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\end{abstract}
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\maketitle
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\tableofcontents
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\section{Introduction} \label{sec:introduction}
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\input{chapters/introduction.tex}
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\section{Methods} \label{sec:methods}
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\input{chapters/methods.tex}
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\section{Results} \label{sec:results}
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\input{chapters/results.tex}
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\section{Conclusion} \label{sec:conclusion}
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\input{chapters/conclusion.tex}
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\section{Perspective} \label{sec:perspective}
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\input{chapters/perspective.tex}
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\bibliography{include/sources/references} % add references to this file
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\end{document}
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