Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates. - Miles D. Cranmer; Richard Galvez; Lauren Anderson; David N. Spergel; Shirley Ho (CoRR, 2019)

Discovering Symbolic Models from Deep Learning with Inductive Biases. - Miles D. Cranmer; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Rui Xu; Kyle Cranmer; David N. Spergel; Shirley Ho (NeurIPS, 2020)

Lagrangian Neural Networks. - Miles D. Cranmer; Sam Greydanus; Stephan Hoyer; Peter W. Battaglia; David N. Spergel; Shirley Ho (CoRR, 2020)

deep21: a Deep Learning Method for 21cm Foreground Removal. - T. Lucas Makinen; Lachlan Lancaster; Francisco Villaescusa-Navarro; Peter Melchior; Shirley Ho; Laurence Perreault Levasseur; David N. Spergel (CoRR, 2020)

Fast and Accurate Non-Linear Predictions of Universes with Deep Learning. - Renan Alves de Oliveira; Yin Li 009; Francisco Villaescusa-Navarro; Shirley Ho; David N. Spergel (CoRR, 2020)

A Bayesian neural network predicts the dissolution of compact planetary systems. - Miles D. Cranmer; Daniel Tamayo; Hanno Rein; Peter W. Battaglia; Samuel Hadden; Philip J. Armitage; Shirley Ho; David N. Spergel (CoRR, 2021)

Multifield Cosmology with Artificial Intelligence. - Francisco Villaescusa-Navarro; Daniel Anglés-Alcázar; Shy Genel; David N. Spergel; Yin Li 009; Benjamin D. Wandelt; Andrina Nicola; Leander Thiele; Sultan Hassan; José Manuel Zorrilla Matilla; Desika Narayanan; Romeel Dave; et al. (CoRR, 2021)

Robust marginalization of baryonic effects for cosmological inference at the field level. - Francisco Villaescusa-Navarro; Shy Genel; Daniel Angles-Alcazar; David N. Spergel; Yin Li 009; Benjamin D. Wandelt; Leander Thiele; Andrina Nicola; José Manuel Zorrilla Matilla; Helen Shao; Sultan Hassan; Desika Narayanan; et al. (CoRR, 2021)

The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence. - Francisco Villaescusa-Navarro; Shy Genel; Daniel Angles-Alcazar; Leander Thiele; Romeel Dave; Desika Narayanan; Andrina Nicola; Yin Li 009; Pablo Villanueva-Domingo; Benjamin D. Wandelt; David N. Spergel; Rachel S. Somerville; et al. (CoRR, 2021)

Inferring halo masses with Graph Neural Networks. - Pablo Villanueva-Domingo; Francisco Villaescusa-Navarro; Daniel Anglés-Alcázar; Shy Genel; Federico Marinacci; David N. Spergel; Lars Hernquist; Mark Vogelsberger; Romeel Dave; Desika Narayanan (CoRR, 2021)

Weighing the Milky Way and Andromeda with Artificial Intelligence. - Pablo Villanueva-Domingo; Francisco Villaescusa-Navarro; Shy Genel; Daniel Anglés-Alcázar; Lars Hernquist; Federico Marinacci; David N. Spergel; Mark Vogelsberger; Desika Narayanan (CoRR, 2021)

The CAMELS project: public data release. - Francisco Villaescusa-Navarro; Shy Genel; Daniel Anglés-Alcázar; Lucia A. Perez; Pablo Villanueva-Domingo; Digvijay Wadekar; Helen Shao; Faizan G. Mohammad; Sultan Hassan; Emily Moser; Erwin T. Lau; Luis Fernando Machado Poletti Valle; et al. (CoRR, 2022)

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; David N. Spergel; Miles D. Cranmer; Nicholas Battaglia; Daniel Anglés-Alcázar; Lars Hernquist; Shirley Ho (CoRR, 2022)

Simple lessons from complex learning: what a neural network model learns about cosmic structure formation. - Drew Jamieson; Yin Li 009; Siyu He; Francisco Villaescusa-Navarro; Shirley Ho; Renan Alves de Oliveira; David N. Spergel (CoRR, 2022)

Field Level Neural Network Emulator for Cosmological N-body Simulations. - Drew Jamieson; Yin Li 009; Renan Alves de Oliveira; Francisco Villaescusa-Navarro; Shirley Ho; David N. Spergel (CoRR, 2022)

The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback. - Digvijay Wadekar; Leander Thiele; J. Colin Hill; Shivam Pandey; Francisco Villaescusa-Navarro; David N. Spergel; Miles D. Cranmer; Daisuke Nagai; Daniel Anglés-Alcázar; Shirley Ho; Lars Hernquist (CoRR, 2022)

Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. - Leander Thiele; Miles D. Cranmer; William R. Coulton; Shirley Ho; David N. Spergel (Mach. Learn. Sci. Technol., 2022)

Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects. - Natalí S. M. de Santi; Francisco Villaescusa-Navarro; L. Raul Abramo; Helen Shao; Lucia A. Perez; Tiago Castro; Yueying Ni; Christopher C. Lovell; Elena Hernandez-Martinez; Federico Marinacci; David N. Spergel; Klaus Dolag; et al. (CoRR, 2023)

SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering. - Pablo Lemos; Liam Holden Parker; ChangHoon Hahn; Shirley Ho; Michael Eickenberg; Jiamin Hou; Elena Massara; Chirag Modi 002; Azadeh Moradinezhad Dizgah; Bruno Régaldo-Saint Blancard; David N. Spergel (CoRR, 2023)

The optical and infrared are connected. - Christian Kragh Jespersen; Peter Melchior; David N. Spergel; Andy D. Goulding; ChangHoon Hahn; Kartheik Iyer (CoRR, 2025)