Speakers
Description
Constructing accurate initial data for binary black hole systems is a critical step in numerical relativity simulations, traditionally addressed through spectral and finite-difference methods. In this talk, we will present Pinndorama, a physics-informed neural network framework for solving the puncture initial data problem, validated against the established numerical code, NRPyElliptic. Pinndorama achieves relative errors on the order of 1e-4 with trained networks delivering full binary black hole initial data in 3D. We will also introduce the application of the Deep Ritz Method in this setting. Our results show that neural networks are effective in finding solutions to this problem while offering a new computational paradigm for constructing initial data in numerical relativity.