A PINN approach learns galactic gravitational potentials from acceleration data, achieving sub-percent errors on simulations while outperforming analytic models and retaining interpretability via structured priors.
N-body simulations of gravitational dynamics
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
We describe the astrophysical and numerical basis of N-body simulations, both of collisional stellar systems (dense star clusters and galactic centres) and collisionless stellar dynamics (galaxies and large-scale structure). We explain and discuss the state-of-the-art algorithms used for these quite different regimes, attempt to give a fair critique, and point out possible directions of future improvement and development. We briefly touch upon the history of N-body simulations and their most important results.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A new multi-scale hierarchical framework in GR uses matter horizons to extend perturbation theory beyond shell-crossing by gluing spacetimes with opposite orientation.
citing papers explorer
-
Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks
A PINN approach learns galactic gravitational potentials from acceleration data, achieving sub-percent errors on simulations while outperforming analytic models and retaining interpretability via structured priors.
-
An essential building block for cosmological zoom-in perturbation theory
A new multi-scale hierarchical framework in GR uses matter horizons to extend perturbation theory beyond shell-crossing by gluing spacetimes with opposite orientation.