Fast and Accurate Non-Linear Predictions of Universes with Deep Learning

Understand

Cosmologists aim to model the evolution of initially low amplitude Gaussian density fluctuations into the highly non-linear "cosmic web" of galaxies and clusters.

• They aim to compare simulations of this structure formation process with observations of large-scale structure traced by galaxies and infer the properties of the dark energy and dark matter that make up 95% of the universe.
• These ensembles of simulations of billions of galaxies are computationally demanding, so that more efficient approaches to tracing the non-linear growth of structure are needed.
• We build a V-Net based model that transforms fast linear predictions into fully nonlinear predictions from numerical simulations.