Simulating the Universe with Machine Learning

09:30-10:00, January 28 @ 2BC

Talk/ Overview

To understand the evolution of the Universe requires a concerted effort of accurate observation of the sky and fast prediction of structures in the Universe. N-body simulation is an effective approach to predicting structure formation of the Universe, though computationally expensive. Here, we build a deep neural network to predict structure formation of the Universe. It outperforms the traditional fast-analytical approximation and accurately extrapolates far beyond its training data. Our study proves that deep learning is an accurate alternative to the traditional way of generating approximate cosmological simulations. Our study shows that one can use deep learning to generate complex 3D simulations in cosmology. This suggests that deep learning can provide a powerful alternative to traditional numerical simulations in cosmology.

Talk/ Speakers

Shirley Ho

Group Leader & Professor at Flatiron Institute

Talk/ Slides

Download the slides for this talk.Download ( PDF, 3940.15 MB)

AMLD / Global partners