Newton vs the machine: solving the chaotic three-body problem using deep neural networks
This enables fast and scalable simulations for astrophysics problems like black-hole binary formation, though it is incremental as it builds on existing numerical methods.
The researchers tackled the chaotic three-body problem by training a deep neural network on numerical solutions, achieving accurate results up to 100 million times faster than state-of-the-art solvers over bounded time intervals.
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 state-of-the-art solver. Our results provide evidence that, for computationally challenging regions of phase-space, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black-hole binary systems or the origin of the core collapse in dense star clusters.