| Full text | |
| Author(s): |
Duarte, Roberta
;
Nemmen, Rodrigo
;
Navarro, Joao Paulo
Total Authors: 3
|
| Document type: | Journal article |
| Source: | Monthly Notices of the Royal Astronomical Society; v. 512, n. 4, p. 14-pg., 2022-04-19. |
| Abstract | |
In this pilot study, we investigate the use of a deep learning (DL) model to temporally evolve the dynamics of gas accreting on to a black hole in the form of a radiatively inefficient accretion flow (RIAF). We have trained a convolutional neural network (CNN) on a data set that consists of numerical solutions of the hydrodynamical equations for a range of initial conditions. We find that deep neural networks trained on one simulation seem to learn reasonably well the spatiotemporal distribution of densities and mass continuity of a black hole accretion flow over a duration of 8 x 10(4)GM/c(3), comparable to the viscous time-scale at r = 400GM/c(2); after that duration, the model drifts from the ground truth suffering from excessive artificial mass injection. Models trained on simulations with different initial conditions show some promise of generalizing to configurations not present in the training set, but also suffer from mass continuity issues. We discuss the caveats behind this method and the potential benefits that DL models offer. For instance, once trained the model evolves an RIAF on a single GPU four orders of magnitude faster than usual fluid dynamics integrators running in parallel on 200 CPU cores. We speculate that a data-driven machine learning approach should be very promising for accelerating simulations of accreting black holes. (AU) | |
| FAPESP's process: | 17/01461-2 - The extreme universe: black holes and the Fermi telescope |
| Grantee: | Rodrigo Nemmen da Silva |
| Support Opportunities: | Research Grants - Young Investigators Grants |