The three-body problem, one of the most notoriously complex calculations in physics, may have met its match in artificial intelligence: a new neural network promises to find solutions up to 100 million times faster than existing techniques. First formulated by Sir Isaac Newton, the three-body problem involves calculating the movement of three gravitationally interacting bodies – such as the Earth, the Moon, and the Sun, for example – given their initial positions and velocities. It might sound simple at first, but the ensuing chaotic movement has stumped mathematicians and physicists for hundreds of years, to the extent that all but the most dedicated humans have tried to avoid thinking about it as much as possible. That's why chronometer time-keepers became more popular for calculating positions at sea rather than using the Moon and the stars – it was just less of a head-scratcher. Today the three-body problem is an important part of figuring out how black hole binaries might interact with single black holes, and from there how some of the most fundamental objects of the Universe interact with each other.

The mind-bending calculations required to predict how three heavenly bodies orbit each other have baffled physicists since the time of Sir Isaac Newton. Now artificial intelligence (A.I.) has shown that it can solve the problem in a fraction of the time required by previous approaches. Newton was the first to formulate the problem in the 17th century, but finding a simple way to solve it has proved incredibly difficult. The gravitational interactions between three celestial objects like planets, stars and moons result in a chaotic system -- one that is complex and highly sensitive to the starting positions of each body. Current approaches to solving these problems involve using software that can take weeks or even months to complete calculations.