Today, we are bringing other exciting results involving black holes and AI. We released a new paper:
"Black Hole Weather Forecasting with Deep Learning: A Pilot Study"
Work by Roberta Duarte (@import_robs), Rodrigo Nemmen (@nemmen) and João Paulo Navarro (from @NVIDIABrasil).
The authors used deep learning to simulate the dynamics of gas accreting onto a black hole, i.e., black hole weather forecasting.
They trained the model (U-Net) with frames from numerical solutions of the hydrodynamical equations.
Numerical simulations are time-consuming. A simple simulation can take as long as 7 days to finish. If we go with more complex simulations, this time may increase.
We want to investigate if deep learning can be a new method to simulate accurately in less time!
In the paper, they discussed two examples:
1- The model simulating only one system after learning only from this system
2- The model simulating an unseen system after training with several systems with different initial conditions
In the first example, they trained the model with a single system and analyzed how the model simulates by iterative predictions.
The result is that the model can simulate up to 8e4 gravitational time accurately with a speed-up of 30000x faster!
