VIDE: The Void IDentification and Examination toolkit. - Paul M. Sutter; Guilhem Lavaux; Nico Hamaus; Alice Pisani; Benjamin D. Wandelt; Michael S. Warren; Francisco Villaescusa-Navarro; Paul Zivick; Qingqing Mao; Benjamin B. Thompson (Astron. Comput., 2015)

From Dark Matter to Galaxies with Convolutional Networks. - Xinyue Zhang; Yanfang Wang; Wei Zhang; Yueqiu Sun; Siyu He; Gabriella Contardo; Francisco Villaescusa-Navarro; Shirley Ho (CoRR, 2019)

HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks. - Juan Zamudio-Fernandez; Atakan Okan; Francisco Villaescusa-Navarro; Seda Bilaloglu; Asena Derin Cengiz; Siyu He; Laurence Perreault Levasseur; Shirley Ho (CoRR, 2019)

Learning neutrino effects in Cosmology with Convolutional Neural Networks. - Elena Giusarma; Mauricio Reyes Hurtado; Francisco Villaescusa-Navarro; Siyu He; Shirley Ho; ChangHoon Hahn (CoRR, 2019)

From Dark Matter to Galaxies with Convolutional Neural Networks. - Jacky H. T. Yip; Xinyue Zhang; Yanfang Wang; Wei Zhang; Yueqiu Sun; Gabriella Contardo; Francisco Villaescusa-Navarro; Siyu He; Shy Genel; Shirley Ho (CoRR, 2019)

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)

Learning the Evolution of the Universe in N-body Simulations. - Chang Chen; Yin Li 009; Francisco Villaescusa-Navarro; Shirley Ho; Anthony Pullen (CoRR, 2020)

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)

Machine Learning and Cosmology. - Cora Dvorkin; Siddharth Mishra-Sharma; Brian Nord; V. Ashley Villar; Camille Avestruz; Keith Bechtol; Aleksandra Ciprijanovic; Andrew J. Connolly; Lehman H. Garrison; Gautham Narayan; Francisco Villaescusa-Navarro (CoRR, 2022)

Learning cosmology and clustering with cosmic graphs. - Pablo Villanueva-Domingo; Francisco Villaescusa-Navarro (CoRR, 2022)

Fast and realistic large-scale structure from machine-learning-augmented random field simulations. - Davide Piras; Benjamin Joachimi; Francisco Villaescusa-Navarro (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)

Robust field-level inference with dark matter halos. - Helen Shao; Francisco Villaescusa-Navarro; Pablo Villanueva-Domingo; Romain Teyssier; Lehman H. Garrison; Marco Gatti; Derek Inman; Yueying Ni; Ulrich P. Steinwandel; Mihir Kulkarni; Eli Visbal; Greg L. Bryan; et al. (CoRR, 2022)

Robust field-level likelihood-free inference with galaxies. - Natalí S. M. de Santi; Helen Shao; Francisco Villaescusa-Navarro; L. Raul Abramo; Romain Teyssier; Pablo Villanueva-Domingo; Yueying Ni; Daniel Anglés-Alcázar; Shy Genel; Elena Hernandez-Martinez; Ulrich P. Steinwandel; Christopher C. Lovell; et al. (CoRR, 2023)

The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites. - Yueying Ni; Shy Genel; Daniel Anglés-Alcázar; Francisco Villaescusa-Navarro; Yongseok Jo; Simeon Bird; Tiziana di Matteo; Rupert A. C. Croft; Nianyi Chen; Natalí S. M. de Santi; Matthew Gebhardt; Helen Shao; et al. (CoRR, 2023)

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)

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets. - Andrea Roncoli; Aleksandra Ciprijanovic; Maggie Voetberg; Francisco Villaescusa-Navarro; Brian Nord (CoRR, 2023)

How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds. - Tri Nguyen; Francisco Villaescusa-Navarro; Siddharth Mishra-Sharma; Carolina Cuesta-Lázaro; Paul Torrey; Arya Farahi; Alex M. Garcia; Jonah C. Rose; Stephanie O'Neil; Mark Vogelsberger; Xuejian Shen; Cian Roche; et al. (CoRR, 2024)

CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning. - Ningyuan Huang; Richard Stiskalek; Jun-Young Lee; Adrian E. Bayer; Charles C. Margossian; Christian Kragh Jespersen; Lucia A. Perez; Lawrence K. Saul; Francisco Villaescusa-Navarro (CoRR, 2025)

Evaluating Retrieval-Augmented Generation Agents for Autonomous Scientific Discovery in Astrophysics. - Xueqing Xu; Boris Bolliet; Adrian Dimitrov; Andrew Laverick; Francisco Villaescusa-Navarro; Licong Xu; Íñigo Zubeldia (CoRR, 2025)

Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery. - Licong Xu; Milind Sarkar; Anto I. Lonappan; Íñigo Zubeldia; Pablo Villanueva-Domingo; Santiago Casas; Christian Fidler; Chetana Amancharla; Ujjwal Tiwari; Adrian E. Bayer; Chadi Ait Ekioui; Miles D. Cranmer; et al. (CoRR, 2025)