A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks (2021-2022)
The numerical integration of a system of N-bodies, the calculation of the mutual forces between bodies can drive the computation time. The computational complexity scales quadratically with the number of bodies. This means that simulating a large system is… expensive.
Machine Learning can help with that. If instead of calculating these forces, we use neural networks to predict the total acceleration, we can save a lot of computing power. Physics-aware neural networks incorporate some of our physics knowledge into the neural network. In this case, we choose Hamiltonian Neural Networks (HNNs) and apply them to the integration of a planetary system with a large number of asteroids. We find that there are advantages and challenges to the application of HNNs to complex cases such as the gravitational N-body problem.
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