29 citations · 39 across the 3 of their papers we have counts for
3 papers
Neural Networks for Nuclear Reactions in MAESTROeX
Duoming Fan, Donald E. Willcox, Christopher DeGrendele +2
We demonstrate the use of neural networks to accelerate the reaction steps in the MAESTROeX stellar hydrodynamics code. A traditional MAESTROeX simulation uses a stiff ODE integrat…
Modelling low Mach number stellar hydrodynamics with MAESTROeX
A. Harpole, D. Fan, M. P. Katz +3
Modelling long-time convective flows in the interiors of stars is extremely challenging using conventional compressible hydrodynamics codes due to the acoustic timestep limitation.…
MAESTROeX: A Massively Parallel Low Mach Number Astrophysical Solver
Duoming Fan, Andrew Nonaka, Ann S. Almgren +2
We present MAESTROeX, a massively parallel solver for low Mach number astrophysical flows. The underlying low Mach number equation set allows for efficient, long-time integration f…