CosmoGAN Neural Network to Study Dark Matter

As cosmologists and astrophysicists delve deeper into the darkest recesses of the universe, their need for increasingly powerful observational and computational tools has expanded exponentially. From facilities such as the Dark Energy Spectroscopic Instrument to supercomputers like Lawrence Berkeley National Laboratory’s Cori system at NERSC, they are on a quest to collect, simulate, and analyze […]

Fast Simulation with Generative Adversarial Networks

In this video from the Intel User Forum at SC18, Dr. Sofia Vallecorsa from CERN openlab presents: Fast Simulation with Generative Adversarial Networks. “This talk presents an approach based on generative adversarial networks (GANs) to train them over multiple nodes using TensorFlow deep learning framework with Uber Engineering Horovod communication library. Preliminary results on scaling of training time demonstrate how HPC centers could be used to globally optimize AI-based models to meet a growing community need.”