Springer Gravitational Wave Science with Machine Learning (Springer Series in Astrophysics and Cosmology)

This book highlights the state of the art of machine learning applied to the science of gravitational waves. The main topics of the book range from the search for astrophysical gravitational wave signals to noise suppression techniques and control systems using machine learning-based algorithms. During the four years of work in the COST Action CA17137-A network for Gravitational Waves, Geophysics and Machine Learning (G2net), the collaboration produced several original publications as well as tutorials and lectures in the training schools we organized. The book encapsulates the immense amount of finding and achievements. It is a timely reference for young researchers approaching the analysis of data from gravitational wave experiments, with alternative approaches based on the use of artificial intelligence techniques.