Hybrid integration of the gravitational N-body problem with Artificial Neural Networks

Studying the evolution of the gravitational N-body problem becomes extremely computationally expensive as the number of bodies increases. In order to alleviate this problem, we study the use of Artificial Neural Networks (ANNs) to substitute expensive parts of the integration of planetary systems. We compare the performance of a Hamiltonian Neural Network (HNN) which includes physics constraints into its architecture with a conventional Deep Neural Network (DNN). We find that HNNs are able to conserve energy better than DNNs in a simplified scenario with two planets, but become challenging to train for a more realistic case, namely when adding asteroids. We develop a hybrid integrator that chooses between the network's prediction and the numerical computation, and show that for a number of asteroids >60, using ANNs improves the computational cost of the simulation while allowing for an accurate reproduction of the trajectory of the bodies.