COSEν

Collective Oscillation Simulation Engine for Neutrinos

COSEν is written in C++ and provides two advanced numerical schemes to simulate collective neutrino oscillations in the mean-field limit. The first method uses fourth order central finite differencing supplimented by third order Kreiss-Oliger dissipation scheme. The second one is implemented with the finite volume method along with the seventh order weighted essentially non-oscillatory scheme for the flux reconstruction across the cell boundaries. In both cases the time evolution is carried out via fourth order Runge-kutta method.

RHINE

R-process Heating Implementation in hydrodynamic simulations with NEural networks

RHINE is a machine-learning based Fortran code for modeling r-process heating in astrophysical hydrodynamic simulations. The code uses trained neural networks to provide fast and accurate estimates of r-process related rates of change for a set of characteristic quantities. RHINE is designed for integration into hydrodynamic simulations of astrophysical environments with r-process viable conditions such as neutron-star mergers. While maintaining a high accuracy comparable to detailed nuclear reaction networks, it avoids their large computational demands.