*AI Learns to Model Our Universe*Kavli Institute for the Physics and Mathematics of the Universe | University of Tokoyo | 2019 Aug 28

...Researchers have successfully created a model of the Universe using artificial intelligence

Researchers seek to understand our Universe by making model predictions to match observations. Historically, they have been able to model simple or highly simplified physical systems, jokingly dubbed the "spherical cows," with pencils and paper. Later, the arrival of computers enabled them to model complex phenomena with numerical simulations. For example, researchers have programmed supercomputers to simulate the motion of billions of particles through billions of years of cosmic time, a procedure known as the N-body simulations, in order to study how the Universe evolved to what we observe today. ...

- A comparison of the accuracy of two models of the Universe. The new deep learning model (left), dubbed D3M, is much more accurate than an existing analytic method (right) called 2LPT. The colors represent the error in displacement at each point relative to the numerical simulation, which is accurate but much slower than the deep learning model. (Credit: S. He et al./PNAS 2019)

At the beginning of our Universe, things were extremely uniform. As time went by, the denser parts grew denser and sparser parts became sparser due to gravity, eventually forming a foam-like structure known as the "cosmic web." To study this structure formation process, researchers have tried many methods, including analytic calculations and numerical simulations. Analytic methods are fast, but fail to produce accurate results for large density fluctuations. On the other hand, numerical (N-body) methods simulate structure formation accurately, but tracking gazillions of particles is costly, even on supercomputers. Thus, to model the Universe, scientists often face the accuracy versus efficiency trade-off. ...

**~ Siyu He**

*Learning to Predict the Cosmological Structure Formation**et al*

*Proceedings of the NAS*116(28):13825 (2019 Jul 09) DOI: 10.1073/pnas.1821458116- arXiv.org > astro-ph > arXiv:1811.06533 > 15 Nov 2018 (v1), 31 Jul 2019 (v2)