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Found 4 papers, 1 papers with code
Cosmological N-body simulations: a challenge for scalable generative models
Our results show that the proposed model produces samples of high visual quality, although the statistical analysis reveals that capturing rare features in the data poses significant problems for the generative models.
Fast cosmic web simulations with generative adversarial networks
Computational models of the underlying physical processes, such as classical N-body simulations, are extremely resource intensive, as they track the action of gravity in an expanding universe using billions of particles as tracers of the cosmic matter distribution.
Accelerating Approximate Bayesian Computation with Quantile Regression: Application to Cosmological Redshift Distributions
This model is trained on a small number of simulations and estimates which regions of the prior space are likely to be accepted into the posterior.
Cosmological model discrimination with Deep Learning
We find that our implementation of DCNN outperforms the skewness and kurtosis statistics, especially for high noise levels.
