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Learning the averaged history of an inhomogeneous universe from its present day density field
In this work, we investigate whether machine learning can be used to infer averaged cosmological quantities directly from the present day matter density distribution. Using an implementation of the simplified silent universe framework, we generate a dataset consisting of 92610 independent relativist
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