A neural net solves the three-body problem 100 million times faster

This article discusses a breakthrough in solving the "three-body problem" using deep learning. The three-body problem is a mathematical challenge that involves calculating and predicting the motion of three celestial bodies under their gravitational forces. Traditional numerical methods struggle to accurately solve this problem due to its complexity. However, researchers have developed a neural network model that can solve the three-body problem more efficiently, making predictions 100 million times faster than previous methods. The article explains how the neural network was trained using a large dataset and highlights the potential applications of this technology in astrophysics and other fields that involve complex dynamical systems.