CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching

Generative machine learning models can learn low-dimensional representations that preserve information needed for downstream tasks. CosmoFlow applies flow matching to cold dark matter simulations, learning compact, interpretable representations for reconstruction, synthetic data generation, and cosmological parameter inference.