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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: [email protected]
Roberta Duarte
Affiliation:
Universidade de São Paulo, Instituto de Astronomia, Geofsica e Ciências Atmosféricas, Departamento de Astronomia, São Paulo, SP 05508-090, Brazil email: [email protected]
João P. Navarro
Affiliation:
NVIDIA
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Abstract

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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

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