Speaker
Description
Accurate Gravitational Wave models require data from Numerical Relativity simulations of compact object mergers to inform them of the waveform behaviour during the non-linear merger phase of the binary evolution. Such simulations require large computational resources to reach valuable resolutions, and are time consuming to perform, restricting their ability to fully explore the parameter space of binary mergers. In this talk I will discuss two recent approaches to incorporating Machine Learning techniques into numerical relativity simulations of binary neutron stars; first to accelerate simulation speeds by modelling the nuclear equation of state with neural networks; and secondly to improve the robustness of low resolution simulations by incorporating machine learning models of small scale physics.