arXiv Machine Learning

21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables

arXiv:2606. 00219v1 Announce Type: cross Abstract: We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust theoretical interpretation frameworks.

arXiv Machine Learning
Aug 6

Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models

arXiv:2512. 10222v2 Announce Type: replace-cross Abstract: Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions.

By Natal\'i S. M. de Santi, Francisco Villaescusa-Navarro, Pablo Araya-Araya, Gabriella De Lucia, Fabio Fontanot, Lucia A. Perez, Manuel Arn\'es-Curto, Violeta Gonzalez-Perez, \'Angel Chandro-G\'omez, Rachel S. Somerville, Tiago Castro
arXiv Machine Learning
Jun 8

Machine Learning for Electron-Scale Turbulence Modeling in W7-X

arXiv:2511. 04567v2 Announce Type: replace-cross Abstract: Constructing reduced models for turbulent transport is essential for accelerating profile predictions and enabling many-query tasks such as parameter exploration and design optimization.

By Ionut-Gabriel Farcas, Don Lawrence Carl Agapito Fernando, Alejandro Banon Navarro, Gabriele Merlo, Frank Jenko