# AI in Gravitational Wave Analysis, an Overview
## 7 Discussion  
Over the past decade, there has been an increasing trend of research papers focusing on the application of artificial intelligence (AI) in the study of gravitational waves. According to the retrieved data from the IEEE, Elsevier, and ACM sources, there has been a substantial increase in the number of papers published each year on the topic, with a total of 300 papers published from 2013 to 2022 (Table 1). The year 2017 marked a significant turning point in the trend, as the number of papers doubled from the previous year. Elsevier showed the highest number of papers, with a total of 245 published between 2013 and 2022, followed by IEEE (with 26) and ACM (with 29). The rise in interest in the application of AI to gravitational wave research is likely due to the increasing availability of data, the development of more advanced AI algorithms, and the growing interest in multi-messenger astronomy. This trend is expected to continue in the future, with AI playing an increasingly important role in the analysis and interpretation of gravitational wave data. Following this brief introduction of research efforts (in terms of published papers in gravitational waves area with artificial intelligence involvement), we conducted an analysis of a set of 49 papers, 2 of them were general reviews of gravitational waves studies, and the remaining 47 dealt with specific works that used at least one AI-based approach for achieving results. Table 2 shows the number of papers and corresponding percentages published in major research areas related to gravitational waves analysis. \begin{table}
\begin{tabular}{c c c c} \hline  & & **Source** & \\
**Year** & **IEEE** & **Elsevier** & **ACM** \\ \hline
2013 & 0 & 1 & 0 \\
2014 & 1 & 1 & 2 \\
2015 & 0 & 7 & 1 \\
2016 & 1 & 16 & 1 \\
2017 & 2 & 26 & 5 \\
2018 & 2 & 16 & 4 \\
2019 & 1 & 37 & 4 \\
2020 & 1 & 40 & 4 \\
2021 & 10 & 47 & 5 \\
2022 & 8 & 54 & 3 \\ \hline Total & 26 & 245 & 29 \\ \hline \end{tabular}
\end{table} 
Table 1: Number of papers grouped by source and year of publication. \begin{table}
\begin{tabular}{c c c} \hline
**Resolved Problem** & **Number of Papers** & **Percentage** \\ \hline Glitch Classification & 10 & 21\% \\ \hline Gravitational Wave Bursts & 4 & 9\% \\ \hline Glitch cancellation/GW & & 13\% \\ denoising & 6 & 13\% \\ \hline Signal Detection (BBHs) & 10 & 21\% \\ \hline Population Studies & 3 & 6\% \\ \hline Data search and query & 1 & 2\% \\ \hline \end{tabular}
\end{table} 
Table 2: Number and percentage of papers grouped by resolved problem. The table indicates that \"Glitch Classification\" and \"Signal Detection (BBHs)\" are two of the most studied areas, each accounting for 21% of the total papers published. \"Glitch cancellation/GW denosing\" and \"CBC--Waveform modeling\" are the next most popular areas, each accounting for 13% and 9% of the papers, respectively. The remaining areas, such as \"Gravitational Wave Bursts\", \"Population Studies\", and \"Parameter Estimation\", account for smaller percentages of the total papers published; \"Improving data quality\" accounts for 6.1% of the analyzed papers. By analyzing the different works, we found that deep learning, despite being the most recent and advanced approach, is the most widely used, with 31 out of 49 papers employing it. It is followed by machine learning (with only 23 papers). For 13 papers, classical statistical methods were used either in combination with or for comparison to AI techniques. Interestingly, there were seven papers that combined deep learning and machine learning techniques. The use of GPUs is not prevalent, with only 15 papers mentioning their use, and 4 of those utilizing multi-GPU techniques to speed up computations. It is worth noting that no other GPU architectures, apart from NVIDIA, were mentioned in the analyzed papers. Table 3 shows the distribution of different NVIDIA GPU architectures mentioned in the analyzed papers, along with their release year, and the overall percentage of mentions.