Cosmology

Latest papers

8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Macrocanonical Generator Networks: Data-Efficient Neural Surrogates for Amortized Physics Simulation
Nov 3, 2026Niall Jeffrey, Benjamin D. WandeltCosmology Neural Surrogate Modeling
Dept. of Physics & King’s Institute for Artificial Intelligence, King’s College London · Dept. of Physics and Astronomy & Dept. of Applied Mathematics and Statistics, Johns Hopkins University
Graph Neural Networks for Astrophysics and Cosmology
Oct 19, 2026Christian Kragh JespersenCosmology Graph Neural Networks
Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA
Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs
Sep 8, 2026James O. Baldwin, Shy Genel, Francisco Villaescusa-NavarroSimulation-Based Inference Graph Neural Networks
City University of New York Graduate Center, 365 5th Ave, New York, NY 10016 · Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, NY 10010, USA · Columbia Astrophysics Laboratory, Columbia University, 550 West 120th Street, New York, NY 10027, USA +1
AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology I: Literature Review
Jul 28, 2026Anamaria Hell, Kateryna Vovk, Veena Krishnaraj +18Cosmology Astrophysics
IPMU
Interpretable Neural Marked Statistics for Cosmological Inference
Jun 9, 2026Federico Semenzato, Benjamin D. Wandelt, Michele Liguori +1Cosmology
Dipartimento di Fisica Galileo Galilei, Universit`a di Padova, I-35131 Padova, Italy · INFN Sezione di Padova, I-35131 Padova, Italy · Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA +2
Beyond AI as Assistants: Toward Autonomous Discovery in Cosmology
May 14, 2026Licong Xu, Thomas BorrettMulti-Agent LLM Systems AI Agents for Scientific Discovery
Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK · Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK · Cavendish Astrophysics, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping
Mar 23, 2026Hubert Leterme, Andreas Tersenov, Jalal Fadili +1Uncertainty Quantification Cosmology
Université Caen Normandie, ENSICAEN, CNRS, Normandie Univ, GREYC UMR 6072, F-14000 Caen, France · Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, 91191, Gif-sur-Yvette, France · Institutes of Computer Science and Astrophysics, Foundation for Research and Technology Hellas (FORTH), Heraklion, Greece
A Fast Generative Framework for High-dimensional Posterior Sampling: Application to CMB Delensing
Mar 4, 2026Hadi Sotoudeh, Pablo Lemos, Laurence Perreault-LevasseurCosmology Posterior Sampling
Department of Physics, Université de Montréal, Montréal, Canada · Mila - Quebec AI Institute, Montréal, Canada · Ciela, Montreal Institute for Astrophysics and Machine Learning, Montréal, Canada +1

Nov 3, 2026J/K move · Enter open · S save

Macrocanonical Generator Networks: Data-Efficient Neural Surrogates for Amortized Physics Simulation

Niall Jeffrey, Benjamin D. Wandelt

Dept. of Physics & King’s Institute for Artificial Intelligence, King’s College London · Dept. of Physics and Astronomy & Dept. of Applied Mathematics and Statistics, Johns Hopkins University

Cosmology Neural Surrogate Modeling Physics-Informed Generative Modeling Physics-Based Simulation

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Many scientific simulations are computationally expensive, limiting their use in simulation-based inference, uncertainty quantification, and decision making. We introduce macrocanonical generator networks, a framework for learning fast, data-efficient neural surrogates for amortized simulation of stationary physical processes by matching multiscale statistics of a target process. Inspired by microcanonical maximum-entropy synthesis, which matches feature statistics per sample by inner-loop optimization, our approach instead trains a neural generator to satisfy the same constraints in expectation, preserving physically realistic sample-to-sample fluctuations. We prove that gradient-descent training induces preconditioned gradient-descent dynamics in sample space that inherit the symmetry-preservation properties of the microcanonical gradient descent of Bruna and Mallat, amortizing its per-sample inner-loop optimization into a single training run after which every sample is generated in one forward pass. We further show that residual generator parameterizations initialized near the identity, trained with a small learning rate, preserve entropy locally during early optimization and mitigate mode collapse. Experiments on cosmology and fluid simulations show that macrocanonical generators recover realistic samples from extremely small training sets, in some cases a single training example, while generating new realizations more than

10^6

times faster per sample than the reference simulator and more than

10^3

times faster than microcanonical gradient descent. The trained generators can be reused for downstream pretraining, including simulation-based inference, providing a data-efficient route to multi-fidelity scientific machine learning workflows.