Learning to predict the cosmological structure formation

DOI10.1073/pnas.1821458116zbMath1431.83191arXiv1811.06533OpenAlexW2900466252WikidataQ92998979 ScholiaQ92998979MaRDI QIDQ5218562

Yin Li, Shirley Ho, Siamak Ravanbakhsh, Yu Feng, Siyu He, Wei Chen, Barnabás Póczos

Publication date: 4 March 2020

Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1811.06533

Mathematics Subject Classification ID

Relativistic cosmology (83F05) Computational methods for problems pertaining to relativity and gravitational theory (83-08)

Related Items (5)

Lossless, scalable implicit likelihood inference for cosmological fields ⋮ Can deep learning distinguish chaos from noise? Numerical experiments and general considerations ⋮ Fast and credible likelihood-free cosmology with truncated marginal neural ratio estimation ⋮ Tutorial on Amortized Optimization ⋮ Likelihood-free inference in state-space models with unknown dynamics

Uses Software

• GADGET
• Adam
• DeepSphere
• U-Net
• V-Net
• FastPM
• nbodykit
• CosmoFlow

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