M. J. Conceição, A. Krone-Martins, A. C. da Silva
In this work, we propose a resolution enhancement methodology for discretized scalar fields and apply it to cosmological N-body simulations. We upscale Dark Matter density 3D Eulerian cubes using supervised machine learning and Principal Component Analysis. Once the low-resolution simulation is performed, our methodology doubles its resolution gaining three orders of magnitude in CPU run times compared to performing the full high-resolution simulation in the same computational system. Moreover, we achieve 98% accuracy in the matter power spectrum compared to the full high-resolution simulation throughout most of the k domain (k<1.0 Mpc h−1) . Finally, the proposed approach is agnostic to the nature of the simulation field. as long as it corresnonds to a 3D scalar field.
2022 IEEE 18th International Conference on e-Science (e-Science)