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Simple lessons from complex learning: what a neural network model learns about cosmic structure formation



Drew Jamieson , Yin Li , Siyu He , Francisco Villaescusa-Navarro , Shirley Ho , Renan Alves de Oliveira , David N. Spergel

* 13 pages, 8 figures 

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Field Level Neural Network Emulator for Cosmological N-body Simulations



Drew Jamieson , Yin Li , Renan Alves de Oliveira , Francisco Villaescusa-Navarro , Shirley Ho , David N. Spergel

* 11 pages, 4 figures 

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Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks



Leander Thiele , Miles Cranmer , William Coulton , Shirley Ho , David N. Spergel

* 11 pages, 5 figures; condensed version accepted at the Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021) as "Equivariant and Modular DeepSets with Applications in Cluster Cosmology" 

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Rediscovering orbital mechanics with machine learning



Pablo Lemos , Niall Jeffrey , Miles Cranmer , Shirley Ho , Peter Battaglia

* 12 pages, 6 figures, under review 

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Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter



Digvijay Wadekar , Leander Thiele , Francisco Villaescusa-Navarro , J. Colin Hill , Miles Cranmer , David N. Spergel , Nicholas Battaglia , Daniel Anglés-Alcázar , Lars Hernquist , Shirley Ho

* Minor updates to Figs. 4 & 8. Added Fig.10. The code and data associated with this paper are available at https://github.com/JayWadekar/ScalingRelations_ML 

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Learned Coarse Models for Efficient Turbulence Simulation



Kimberly Stachenfeld , Drummond B. Fielding , Dmitrii Kochkov , Miles Cranmer , Tobias Pfaff , Jonathan Godwin , Can Cui , Shirley Ho , Peter Battaglia , Alvaro Sanchez-Gonzalez


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