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.
Enter the neural network produced by researchers from the University of Edinburgh and the University of Cambridge in the UK, the University of Aveiro in Portugal, and Leiden University in the Netherlands.
The team developed a deep artificial neural network (ANN), trained on a database of existing three-body problems, plus a selection of solutions that have already been painstakingly worked out. The ANN was shown to have a lot of promise for reaching accurate answers much more quickly than we can today.
"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," write the researchers in their paper.
neural pathways in the brain 大腦中的神經傳導路徑
文中an artificial neural network (ANN)，指“人工神經網絡”