The First AI Simulation of the Universe is Fast and Accurate — and We Don’t Know Why

A full understanding of the evolution of the universe’s structure is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the universe and compare the findings to computer simulations. Simulating the movement of billions of particles over billions of years is a daunting task, however, even when using the simplest physical models.

In this lecture, Shirley Ho will discuss her team’s work building a deep neural network that learns from a set of pre-run numerical simulations and predicts the large scale structure of the universe. Extensive analysis demonstrates that their deep-learning technique outperforms the commonly used fast approximate simulation method in predicting cosmic structure in the non-linear regime. They also show that their method can accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. This ability to extrapolate outside its training set is highly unexpected and remains a mystery.