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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Super-Resolution Emulation of Large Cosmological Fields with a 3D Conditional Diffusion Model

Adam Rouhiainen · Michael Gira · Gary Shiu · Kangwook Lee · Moritz Münchmeyer


High-resolution (HR) simulations of baryonic matter in cosmology often take millions of CPU hours. On the other hand, low resolution (LR) dark matter simulations of the same comological volume use minimal computing resources. In this paper we train a conditional diffusion model to upgrade LR dark matter simulations probabilistically to HR baryonic matter simulations. Our approach is based on the Palette diffusion model, which we generalize to 3 dimensions. Our superresolution emulator is trained to perform outpainting, and can upgrade arbitrarily large cosmological volumes from LR to HR, using an iterative outpainting procedure.

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