PINNGraPE: Physics Informed Neural Network for Gravitational wave Parameter Estimation

This GR24/Amaldi 16 proceedings paper is a current summary of the PINNGraPE implementation for rapid and accurate gravitational wave parameter estimation using physics-informed neural networks, a work-in progress with Leigh Smith, Matteo Scialpi and Francesco di Clemente (see the arXiv preprint for more details).

Ultimately, we aim to produce a method which will complement existing weakly-modeled searches, such as the Coherent WaveBurst (cWB) pipeline.