The field is distinct from computer science (the mathematical study of computation, computers and information processing). It is also different from theory and experiment which are the traditional forms of science and engineering. The scientific computing approach is to gain understanding, mainly through the analysis of mathematical models implemented on computers.
Scientists and engineers develop computer programs, application software, that model systems being studied and run these programs with various sets of input parameters. Typically, these models require massive amounts of calculations (usually floating-point) and are often executed on supercomputers or distributed computing platforms.
Numerical analysis is an important underpinning for techniques used in computational science.
Problem domains for computational science/scientific computing include:
Numerical simulations have different objectives depending on the nature of the task being simulated:
Reconstruct and understand known events (e.g., earthquake, tsunamis and other natural disasters).
Predict future or unobserved situations (e.g., weather, sub-atomic particle behaviour).
Model fitting and data analysis
Appropriately tune models or solve equations to reflect observations, subject to model constraints (e.g. oil exploration geophysics, computational linguistics)
Use graph theory to model networks, especially those connecting individuals, organizations, and websites.
Optimize known scenarios (e.g., technical and manufacturing processes, front end engineering).