Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

This chapter provides a summary of recent developments harnessing the data revolution to realize the science goals of Gravitational Wave Astrophysics. This is an exciting journey that is powered by the renaissance of artificial intelligence, and a new generation of researchers that are willing to embrace disruptive advances in innovative computing and signal processing tools. In this chapter, machine learning refers to a class of algorithms that can learn from data to solve new problems without being explicitly re-programmed. While traditional machine learning algorithms, e.g., random forests, nearest neighbors, etc., have been used successfully in many applications, they are limited in their ability to process raw data, usually requiring time-consuming feature engineering to preprocess data into a suitable representation for each application. On the other hand, deep learning algorithms can learn patterns from unstructured data, finding useful representations and automatically extracting relevant features for each application. The ability of deep learning to deal with poorly defined abstractions and problems has led to major advances in image recognition, speech, computer vision applications, robotics, among others [1]. The following sections describe a few noteworthy applications of modern machine learning for gravitational wave modeling, detection and inference. It is the expectation that by the time this chapter is published, the ongoing developments at the interface of artificial intelligence and extreme-scale computing will have leapt forward, making this chapter a reminiscence of a fast-paced, evolving field of research. The chapter concludes with a summary of recent applications at the interface of deep learning and high performance computing to address computational grand challenges in Gravitational Wave Astrophysics.