# AI in Gravitational Wave Analysis, an Overview
## 1 Introduction
However, dealing with noise becomes particularly challenging when it exhibits non-stationary characteristics and possesses a variable probability distribution. Researchers have dedicated extensive efforts to overcome these challenges by developing advanced algorithms and techniques. Through the application of adaptive filters, time-frequency analyses, and sophisticated machine learning methods, scientists have made significant progress in denoising gravitational wave signals and enhancing the accuracy of measurements [15; 16]. The detection and analysis of binary black hole signals are a compelling area of research within gravitational wave studies. Binary black holes serve as captivating astrophysical objects, and the identification of their gravitational wave signals provides valuable insights into the nature, formation, and evolution of these enigmatic entities. Detecting these signals amidst the background noise presents a significant challenge as binary black hole signals are orders of magnitude weaker than the characteristic amplitude of the noise. Researchers have leveraged various data analysis methods, including matched filtering and template matching techniques, to identify and extract these elusive signals from the data captured by gravitational wave detectors [17]. This research has not only contributed to a better understanding of binary black holes but has also enriched our knowledge of the larger cosmic landscape. Gravitational wave bursts constitute another intriguing research area. These transient events, characterized by short-duration bursts of gravitational waves, can arise from cataclysmic phenomena, such as supernovae or compact object mergers. Detecting and characterizing these bursts provide crucial information about the underlying astrophysical processes and the sources responsible for their generation. Researchers have developed specialized algorithms and signal processing techniques, including time-frequency analyses and deep learning approaches, to identify and study gravitational wave bursts like in [18]. The study of these events not only broadens our understanding of the cosmos but also provides insights into the dynamics and behaviors of cataclysmic astrophysical events. In addition to the aforementioned core research problems, several minor issues contribute to the comprehensive analysis of gravitational wave data. These include issuessuch as data preprocessing, noise characterization, modeling, signal parameter estimation, event localization, and data visualization. Addressing these minor challenges collectively enhances the overall accuracy, efficiency, and interpretability of the gravitational wave data analysis process. Overcoming these research problems and challenges would not have been possible without the remarkable advancements in artificial intelligence, deep learning, and machine learning techniques. These technologies have played a pivotal role in enabling the identification of weak signals buried within noise, the accurate classification of various signal types, and the cancellation of unwanted noise sources [19]. The graphics processing unit (GPU) has emerged as a critical hardware component in accelerating the computations required for deep learning and machine learning models. Furthermore, dedicated software frameworks and libraries, such as TensorFlow, PyTorch, and Keras, have been explicitly developed for the analysis of gravitational wave data, facilitating efficient and effective implementation of these advanced techniques. This paper aims to comprehensively explore the applications, technical implementations and hardware acceleration, of artificial intelligence, with a key focus on deep learning, in addressing the main research problems associated with gravitational wave astronomy. It will delve into the methodologies and algorithms employed in glitch classification, glitch cancellation, binary black hole signal detection, gravitational wave bursts, and other minor challenges.