Hybrid Physical-Neural ODEs for Fast N-body Simulations
Abstract
We present a new scheme to compensate for the small-scales approximations resulting from Particle-Mesh (PM) schemes for cosmological N-body simulations. This kind of simulations are fast and low computational cost realizations of the large scale structures, but lack resolution on small scales. To improve their accuracy, we introduce an additional effective force within the differential equations of the simulation, parameterized by a Fourier-space Neural Network acting on the PM-estimated gravitational potential. We compare the results for the matter power spectrum obtained to the ones obtained by the PGD scheme (Potential gradient descent scheme). We notice a similar improvement in term of power spectrum, but we find that our approach outperforms PGD for the cross-correlation coefficients, and is more robust to changes in simulation settings (different resolutions, different cosmologies).
Keywords
Cite
@article{arxiv.2207.05509,
title = {Hybrid Physical-Neural ODEs for Fast N-body Simulations},
author = {Denise Lanzieri and François Lanusse and Jean-Luc Starck},
journal= {arXiv preprint arXiv:2207.05509},
year = {2022}
}
Comments
Accepted at the ICML 2022 Workshop on Machine Learning for Astrophysics. Updated version with link to the source code