Francisco Villaescusa-Navarro

Orcid: 0000-0002-4816-0455

According to our database1, Francisco Villaescusa-Navarro authored at least 27 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds.
CoRR, 2024

2023
Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets.
CoRR, 2023

Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects.
CoRR, 2023

The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites.
CoRR, 2023

Robust field-level likelihood-free inference with galaxies.
CoRR, 2023

2022
Robust field-level inference with dark matter halos.
CoRR, 2022

The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback.
CoRR, 2022

Field Level Neural Network Emulator for Cosmological N-body Simulations.
CoRR, 2022

Simple lessons from complex learning: what a neural network model learns about cosmic structure formation.
CoRR, 2022

Fast and realistic large-scale structure from machine-learning-augmented random field simulations.
CoRR, 2022

Learning cosmology and clustering with cosmic graphs.
CoRR, 2022

Machine Learning and Cosmology.
CoRR, 2022

Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter.
CoRR, 2022

The CAMELS project: public data release.
CoRR, 2022

2021
Weighing the Milky Way and Andromeda with Artificial Intelligence.
CoRR, 2021

Inferring halo masses with Graph Neural Networks.
CoRR, 2021

The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence.
CoRR, 2021

Robust marginalization of baryonic effects for cosmological inference at the field level.
CoRR, 2021

Multifield Cosmology with Artificial Intelligence.
CoRR, 2021

2020
Learning the Evolution of the Universe in N-body Simulations.
CoRR, 2020

Fast and Accurate Non-Linear Predictions of Universes with Deep Learning.
CoRR, 2020

deep21: a Deep Learning Method for 21cm Foreground Removal.
CoRR, 2020

2019
From Dark Matter to Galaxies with Convolutional Neural Networks.
CoRR, 2019

Learning neutrino effects in Cosmology with Convolutional Neural Networks.
CoRR, 2019

HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks.
CoRR, 2019

From Dark Matter to Galaxies with Convolutional Networks.
CoRR, 2019

2015
VIDE: The Void IDentification and Examination toolkit.
Astron. Comput., 2015


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