Nicolas Payot

Co-supervisor

GitHub

Publications

PM++: Scaling Differentiable Cosmological Simulators with Dynamic Particle Ownership

Large differentiable particle-mesh simulations support cosmological inference and simulator-coupled learning, but their evolving states can … (voir plus)exceed single-accelerator memory. Distribution with fixed particle ownership remains memory-limited because deposition halos must cover the maximum cumulative displacement. We present

2026-10-02

Sim2Sci @ Neural Information Processing Systems (publié)

openreview.net

Predicting the Subhalo Mass Functions in Simulations from Galaxy Images

Andreas Filipp

Tri Nguyen

J. Rose

Chris Lovell

Francisco Villaescusa-navarro

2025-10-15

ArXiv (prépublication)

arxiv.org

Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem

Mario Pasquato

Alessandro Alberto Trani

Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. Machine learning (ML) is increasingly deployed to pred… (voir plus)ict the evolution of such systems, e.g. with the goal of speeding up simulations. Strategies such as active Learning (AL) are a natural choice to optimize ML training. Here we showcase an AL failure when predicting the stability of the Sitnikov three-body problem, the simplest case of N-body problem displaying chaotic behavior. We link this failure to the fractal nature of our classification problem's decision boundary. This is a potential pitfall in optimizing large sets of N-body simulations via AL in the context of star cluster physics, galactic dynamics, or cosmology.

2023-11-28

arXiv (prépublication)

doi.org

arxiv.org

Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies

Pablo Lemos

Carolina Cuesta-Lazaro

Chirag Modi