That this AI can simulate universes in 30ms is not the scary part. It’s that its creators don’t know why it works so well
Neural networks can build 3D simulations of the universe in milliseconds, compared to days or weeks when using traditional supercomputing methods, according to new research.
To study how stuff interacts in space, scientists typically build computational models to simulate the cosmos. One simulation approach – known as N-body simulation – can be used to recreate phenomena ranging from smaller events, such as the collapse of molecular clouds into stars, to a giant system, such as the whole universe, obviously to varying levels of accuracy and resolution.
The individual interactions between each of the millions or billions of particles or entities in these models have to be repeatedly calculated to track their motion over time. This requires heavy amounts of compute power, and it takes several days or weeks for a supercomputer to return the results.
For impatient boffins, there’s now some good news. A group of physicists, led by eggheads at the Center for Computational Astrophysics at the Flatiron Institute in New York, USA, decided to see if neural networks could speed things up a bit.
They successfully built a deep neural network dubbed the deep density displacement model, or DM, according to a paper published this week in the Proceedings of the National Academy of Sciences of the United States of America. The model creates simulations of the universe after being given a set of displacement vectors for the particles in the system: these vectors, simply put, define the direction and distance the particles should be heading as the universe expands over time. It then turns these vectors into images that show how particles of matter clump together to form webs of galaxy filaments over time, under the effects of gravity.
A total of 8,000 universe simulations, each containing 32,728 particles spread over a virtual space spanning 600 million light years, were produced by traditional software and used to train DM.
