We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic
pretraining approach for physical surrogate modeling. MPP involves training large surrogate …

Generative adversarial networks (GANs) are frequently utilized in astronomy to construct an
emulator of numerical simulations. Nevertheless, training GANs can prove to be a …

Abstract Simulation-Based Inference of Galaxies (SimBIG) is a forward modeling framework
for analyzing galaxy clustering using simulation-based inference. In this work, we present …

The studies of disordered heterogeneous media and galaxy cosmology share a common
goal: analyzing the disordered distribution of particles and/or building blocks at microscales …

We use explainable neural networks to connect the evolutionary history of dark matter halos
with their density profiles. The network captures independent factors of variation in the …

The standard Λ CDM cosmological model predicts the presence of cold dark matter, with the
current accelerated expansion of the Universe driven by dark energy. This model has …

Recently, hybrid bias expansions have emerged as a powerful approach to modelling the
way in which galaxies are distributed in the Universe. Similarly, field-level emulators have …

Context. Accurate mock halo catalogues are indispensable data products for developing
and validating cosmological inference pipelines. A major challenge in generating mock …

In this work, we extend our recently developed super-resolution (SR) model for cosmological
simulations to produce fully time-consistent evolving representations of the particle phase …

Score-based generative models have emerged as alternatives to generative adversarial
networks (GANs) and normalizing flows for tasks involving learning and sampling from …