Machine learning accelerates cosmological simulations

A universe evolves over billions and billions of years, but researchers have now developed a way to create a complex simulated universe in less than a day. The technique, published in Proceedings of the National Academy of Sciences, brings together machine learning, high-performance computing and astrophysics — and will help usher in a new era of high-resolution cosmology simulations. The U.S. National Science Foundation funded the research.

Cosmological simulations are an essential part of teasing out the many mysteries of the universe, including those of dark matter and dark energy. But until now, researchers faced the common conundrum of not being able to have it all — simulations could either focus on a small area at high resolution or encompass a large volume of the universe at low resolution.

Carnegie Mellon University physicist Tiziana Di Matteo and colleagues surmounted this problem by teaching a machine learning algorithm based on neural networks to upgrade a simulation from low resolution to super resolution.

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