Astrophysics researchers have long faced a tradeoff when simulating space�� simulations could be either high-resolution or cover a large swath of the universe. With the help of generative adversarial networks, they can accomplish both at once. Carnegie Mellon University and University of California researchers developed a deep learning model that upgrades cosmological simulations from low to high��
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