All areas of astronomy and astrophysics rely on computation to look at,
manipulate, and understand data. The field of Computational
Astrophysics is distinct in that its goal is to address problems in astronomy and astrophysics
where observational data is inaccessible to instruments. This can happen when the
physical processes of astrophysical interest occur deep inside the
observed object. Computational
astrophysics also allows us to address questions where extreme heat and density, or extreme length
and time scales are involved, so that complete observations are unavailable and laboratory experiments on earth are limited.
Researchers working in the field of computational astrophysics formulate
a theoretical model of the physical process. Theoretical models are
typically complex, so that testing the model, or using the model to make
predictions requires solving the governing nonlinear partial
differential equations using large-scale simulation.
Many astrophysical phenomena can be approximated using a fluid approach.
In this case hydrodynamic or magnetohydrodynamic (MHD) equations
consisting of conservation laws for mass, momentum, and energy, and
magnetic field must be solved. These equations may be solved on an Eulerian grid using
pseudospectral methods, finite-element methods (FEM), or finite-volume
methods. They may also be solved using Lagrangian methods which do not use a grid, for example with
arbitrary Lagrangian-Eulerian (ALE) codes, or smoothed particle
hydrodynamics (SPH) codes.
Other astrophysical phenomena require an approach that includes
specifics of the particle dynamics. These approaches require solution
of a kinetic equation for the distribution of particles in position,
velocity, and time. Variations on this type of equations are called the
Boltzmann equation, the Vlasov-Poisson equations, or the Vlasov-Maxwell
equations. Computational astrophysicists use numerical methods such as
Monte Carlo or particle-in-cell (PIC) methods to solve these types of
equations.
Because of the complexity of equations for astrophysical modeling, the use of a computer
designed for parallel computation is needed. Typical simulations use thousands of compute
nodes, with each node multi-threaded, and are performed at national and international
super-computing facilities. Both message passing interface (MPI) and
programs for multi-threading (OpenMP) are used, while more
efficient programming solutions are constantly being developed.
Researchers in the field of Computational Astrophysics are deeply
invested in extending existing numerical techniques and developing
innovative, new approaches to capture ever more complex physics in
computer simulations.
Thus in the field of Computational Astrophysics, the majority of the data
we examine is simulation data, produced from theoretical models. It is important that
this data be carefully processed so that comparison with limited observational
data can be made. This comparison allows us to make predictions for the future, and to
improve theoretical models. Those models provide a framework for understanding
observational data.
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