Deep learning for cosmology: parameter measurement and generation of simulations

Deep learning-based analysis methods are gaining interest in cosmology due to
their unique ability to create very rich and complex models. These models are 
particularly well suited for analysis of large scale structure data, as the matter 
density fields are comprised of highly nonlinear, complicated features, such as 
halos, filaments, sheets and voids.
But can this information be utilised by the deep learning algorithm to gain a 
better understanding of the cosmological model? In this talk I will present the 
application of Convolutional Neural Networks (CNNs) for constraining 
cosmological parameters. I will compare the constraining power against the 
commonly used statistic, the power spectrum, and explore different regimes
in quality of data and simulations.
Finally, I will introduce the Generative Adversarial Networks (GANs): a 
CNN-based technique, which can learn from a training set and then generate 
new, statistically similar data. I will present a study of applying GANs to generating 
samples of the cosmic web and discuss the prospects of applying them to 
render 2D and 3D N-body  simulation - like data.