# AI in Gravitational Wave Analysis, an Overview
## 6 Minor Problems and Tasks  
In addition to classical problems, as detailed in previous sections, there are smaller classes of gravitational wave problems and tasks that have proved particularly suitable for analysis with artificial intelligence models. This area includes compact binary coalescence, data generation for glitch identification and classification, and the study of source populations to discriminate the generative sources of gravitational waves. The modeling of gravitational wave bursts is suitable for generative models, like GANs. In fact, ref. [54] introduces generative adversarial networks (GANs) to generate generalized gravitational wave bursts. The paper presents an approach based on latent space exploration to combine characteristics from five classes of waveforms to generate new hybrid waveforms. Thanks to adversarial patterns, the GAN learns to generate realistic and diverse gravitational wave burst signals. Other works, such as [55], employed machine learning techniques to rapidly generate waveform templates for LISA data analysis. Specific algorithms, such as order-reduction, have been used to approximate and interpolate the complex dynamics of extreme mass ratio inspiral (EMRI) waveforms, allowing for efficient waveform generation. Ref. [56] leverages deep generative models, specifically conditional autoencoders, to model gravitational waveforms. Using the generative capabilities of autoencoders and conditional autoencoders, authors can generate synthetic waveforms based on desired conditions. Generating a waveform could be similar to modeling a natural language phrase, as ref. [57] demonstrates using deep seq2 seq models, to analyze and understand the merging and ring-down phases of binary black hole coalescences. This class of models, mostly used for natural language processing, can generate waveforms as sequences of tokens. Machine learning and deep learning techniques offer promising solutions for improving data quality in glitch detection/classification. These methods enable the automatic extraction of relevant features from data, allowing for the accurate identification and classification of glitches. Supervised learning algorithms can be trained on labeled glitch data to create robust models that distinguish between different types of glitches. Unsupervised learning approaches can discover underlying patterns in unlabeled data, aiding in the identification of novel or unknown glitches. By leveraging machine learning and deep learning, researchers can enhance the accuracy and efficiency of glitch detection/classification, leading to improved data quality and more reliable scientific measurements. Refs. [20; 58] present projects focused on the study of gravitational wave noise. These works utilize machine learning algorithms in combination with citizen science participation to improve the performance of gravitational wave detectors. Machine learning models assist in identifying and classifying gravitational wave signals, while citizen scientists contribute to data validation and labeling, collectively enhancing the detector's sensitivity and efficiency. In order to increase data variability, GANs have been used to generate new synthetic glitches; ref. [59] employs GANs to generate synthetic transient noise artifacts in gravitational wave detector data. GANs are trained on real data to learn the statistical properties of noise artifacts and generate realistic synthetic samples. This allows researchers to study and develop strategies to mitigate the impact of transient noise on gravitational wave analyses. Ref. [60] presents an unsupervised learning architecture for classifying transient noise in interferometric gravitational wave detectors. Through an unsupervised approach, the model can learn patterns and features in the noise data, enabling the identification and categorization of different types of noise sources without requiring labeled training data.