The speed and accuracy of a project at the Flatiron Institute, called the Deep Density Displacement Model, or D3M for short, wasn’t the biggest surprise to astrophysicists who used artificial intelligence techniques to generate complex 3-D simulations of the universe in an amazing 30 milliseconds, including how much of the cosmos is dark matter.
The real shock was that D3M could accurately simulate how the universe would look if certain parameters were tweaked even though the model had never received any training data where those parameters varied.
“We can run these simulations in a few milliseconds, while other ‘fast’ simulations take a couple of minutes,” says study co-author Shirley Ho, a group leader at the Flatiron Institute Center for Computational Astrophysics in New York City and an adjunct professor at Carnegie Mellon University.
Such studies require running thousands of simulations, making a lightning-fast and highly accurate computer model one of the major objectives of modern astrophysics.
Ho, He and their colleagues honed the deep neural network that powers D3M by feeding it 8,000 different simulations from one of the highest-accuracy models available.
After training D3M, the researchers ran simulations of a box-shaped universe 600 million light-years across and compared the results to those of the slow and fast models.
Whereas the slow-but-accurate approach took hundreds of hours of computation time per simulation and the existing fast method took a couple of minutes, D3M could complete a simulation in 30 milliseconds.