Black Hole Weather Forecasting Using Deep Learning

Abstract

Traditional methods of studying accretion flows onto black holes mainly consist of computationally expensive numerical simulations. This often imposes severe limitations to the dimensionality, simulation times, and resolution. Computational astrophysics is in urgent need of new tools to accelerate the calculations, thereby leading to faster results. We propose a deep learning method to make black hole "weather forecasting": a datadriven approach for solving the chaotic dynamics of BH accretion flows. Our model can reproduce the results of a hydrodynamic simulation with an error < 3% and at the same time speeding-up the calculations by a factor of 10 4.5 , thus reducing the simulation time.