arXiv Machine Learning July 31, 2026 By Cooper Jacobus, Beatriz Tucci, Oliver Philcox

Emulating Cosmic Structure Formation with a Lagrangian Neural Cellular Automaton

Read the original on arXiv Machine Learning →

arXiv:2607. 27320v1 Announce Type: cross Abstract: Field-level inference of cosmological initial conditions from galaxy surveys requires a forward model that is simultaneously accurate in the non-linear regime, computationally efficient, and fully differentiable.

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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 10

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions

arXiv:2606. 10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data.

By Ludvig Doeser, Jens Jasche

arXiv Machine Learning

Aug 14

History-informed Lagrangian Neural Networks

arXiv:2608. 13215v1 Announce Type: new Abstract: Forecasting the long-horizon evolution of mechanical systems from position-only observations is a pivotal yet difficult task, as hidden velocities and trajectory-specific physical properties must be inferred simultaneously.

By Tianshuo Zhang, Xianglei Xing, Wenzhe Zhai, Jia Gao, He Cao

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arXiv Computer Vision

Sep 10

Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization

arXiv:2609.10464v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architecture (JEPA) world models learn a compact latent representation of the world that supports prediction and planning,...

By Andy Zeyi Liu, Haoran Sun, Lucas Baker, Randall Balestriero, John Sous

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arXiv AI

Sep 10

Field-level weak lensing cosmology with $60$ simulations using multifidelity simulation-based inference

arXiv:2606.23346v2 Announce Type: replace-cross Abstract: We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using just 60 $N$-body si...

By Alex A. Saoulis, Kiyam Lin, Niall Jeffrey, Maximilian von Wietersheim-Kramsta, Davide Piras, Alessio Spurio Mancini, Ana M. G. Ferreira, Benjamin Joachimi

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arXiv Machine Learning

Jun 2

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP

arXiv:2605. 27527v2 Announce Type: replace-cross Abstract: Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves.

By Siddharth Chaini, Federica B. Bianco, Ashish Mahabal

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

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