Investigations into new machine learning techniques for gravitational wave astronomy

The field of gravitational wave astronomy is currently benefiting from the rapid growth of machine learning applications to problems such as classification (detection or model selection) and regression (parameter estimation). So far, only these areas of gravitational wave data analysis have been investigated since there is a direct relation between them and the most common and well established machine learning processes. In this project we propose the investigation of a new (known) form of machine learning known as Physics Informed Neural Networks (PINNs) in which machine learning is used to obtain accurate models of functions that are particular solutions to physically informed problems. For example, such problems include gravitational waveforms modelled by Einsteins General Relativity, solutions to the Tolman Oppenheimer Volkoff Equation for neutron stars, solving geodesic paths for particles in a general relativistic spacetime, and solving the spacetime metric for arbitrary mass-energy distributions. The student will investigate the potential for PINNs amongst these and other problems in the gravitational wave field.