Hostname: page-component-77c89778f8-swr86 Total loading time: 0 Render date: 2024-07-23T07:10:57.805Z Has data issue: false hasContentIssue false
  • English
  • Français

The first AI simulation of a black hole

Published online by Cambridge University Press:  29 March 2021

Rodrigo Nemmen Open the ORCID record for Rodrigo Nemmen [Opens in a new window] ,
Roberta Duarte  and
João P. Navarro
Rodrigo Nemmen
Affiliation:
Universidade de São Paulo, Instituto de Astronomia, Geofsica e Ciências Atmosféricas, Departamento de Astronomia, São Paulo, SP 05508-090, Brazil email: rodrigo.nemmen@iag.usp.br
Roberta Duarte
Affiliation:
Universidade de São Paulo, Instituto de Astronomia, Geofsica e Ciências Atmosféricas, Departamento de Astronomia, São Paulo, SP 05508-090, Brazil email: rodrigo.nemmen@iag.usp.br
João P. Navarro
Affiliation:
NVIDIA
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We report the results from our ongoing pilot investigation of the use of deep learning techniques for forecasting the state of turbulent flows onto black holes. Deep neural networks seem to learn well black hole accretion physics and evolve the accretion flow orders of magnitude faster than traditional numerical solvers, while maintaining a reasonable accuracy for a long time.

Keywords

Black hole physicsastrostatisticsactive galactic nuclei
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S359: Galaxy Evolution and Feedback across Different Environments , March 2019 , pp. 329 - 333
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of International Astronomical Union

References

Almeida, I. & Nemmen, R. 2020, MNRAS, 492, 2553 CrossRefGoogle Scholar
Cybenko, G. 1989, Math. of Cont., Sign. and Sys.Google Scholar
Goodfellow, I., Bengio, Y., & Courville, A. 2016, The MIT Press Google Scholar
Hausen, R. & Robertson, B. E. 2020, ApJS, 60, 84 Google Scholar
Jaeger, H. & Haas, H. 2004, Science, 304, 78 CrossRefGoogle Scholar
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. 2014 CVPR Google Scholar
Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2017, Commun. ACM, 60, 84 10.1145/3065386CrossRefGoogle Scholar
LeCun, Y., Bengio, Y., & Hinton, G. 2015, Nature, 521, 436 10.1038/nature14539CrossRefGoogle Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., et al. 2015, Nature, 518, 529 CrossRefGoogle Scholar
Nemmen, R. 2019, ApJL, 880, L26 CrossRefGoogle Scholar
Porth, O. & others 2019, ApJS, 243, 26 10.3847/1538-4365/ab29fdCrossRefGoogle Scholar
Silver, D., Huang, A., Maddison, C. J., et al. 2016, Nature, 529, 484 CrossRefGoogle Scholar
Tompson, J., Schlachter, K., Sprechmann, P., et al. 2016, arXiv:1607.03597Google Scholar
Zhang, X., Wang, Y., Zhang, W., et al. 2019, arXiv:1902.05965Google Scholar