We review the field of collisionless numerical simulations for the large-scale structure of the
Universe. We start by providing the main set of equations solved by these simulations and …

We present an extension of our recently developed Wasserstein optimized model to emulate
accurate high-resolution (HR) features from computationally cheaper low-resolution (LR) …

The goal of generative models is to learn the intricate relations between the data to create
new simulated data, but current approaches fail in very high dimensions. When the true data …

Fast and accurate simulations of the nonlinear evolution of the cosmic density field are a
major component of many cosmological analyses, but the computational time and storage …

Abstract Recently, Generative Adversarial Networks (GANs) trained on samples of
traditionally simulated collider events have been proposed as a way of generating larger …

In recent years, deep learning models have been successfully employed for augmenting low-
resolution cosmological simulations with small-scale information, a task known as" super …

Recently, a type of neural networks called generative adversarial networks (GANs) has been
proposed as a solution for the fast generation of simulation-like datasets in an attempt to …

ABSTRACT A major aim of cosmological surveys is to test deviations from the standard CDM
model, but the full scientific value of these surveys will only be realized through efficient …

Cosmological observations provide our most robust evidence for dark matter that is
(approximately) collisionless and cold, and furthermore can provide powerful tests of the non …

Measuring the sum of the three active neutrino masses, M ν, is one of the most important
challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on …