Newton vs the machine: solving the chaotic threebody problem using deep neural networks
Abstract
Since its formulation by Sir Isaac Newton, the problem of solving the equations of motion for three bodies under their own gravitational force has remained practically unsolved. Currently, the solution for a given initialization can only be found by performing laborious iterative calculations that have unpredictable and potentially infinite computational cost, due to the system's chaotic nature. We show that an ensemble of solutions obtained using an arbitrarily precise numerical integrator can be used to train a deep artificial neural network (ANN) that, over a bounded time interval, provides accurate solutions at fixed computational cost and up to 100 million times faster than a stateoftheart solver. Our results provide evidence that, for computationally challenging regions of phasespace, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of manybody systems to shed light on outstanding phenomena such as the formation of blackhole binary systems or the origin of the core collapse in dense star clusters.
 Publication:

arXiv eprints
 Pub Date:
 October 2019
 arXiv:
 arXiv:1910.07291
 Bibcode:
 2019arXiv191007291B
 Keywords:

 Astrophysics  Astrophysics of Galaxies;
 Astrophysics  Solar and Stellar Astrophysics;
 Computer Science  Machine Learning;
 Physics  Computational Physics
 EPrint:
 6 pages, 6 figures