Among examples of the most significant developments in algorithms are the widely used Metropolis, FFT and multigrid algorithms. The Metropolis algorithm grew out of physical chemistry in 1950's through attempts to calculate statistical properties of chemical reactions. It is now used in a wide range of areas, including astrophysics, many areas of engineering, and chemistry. The FFT (Fast Fourier Transform), an implementation of Fourier Analysis, is used in signal processing, image processing, seismology, physics, radiology, acoustics and many other areas. The more recent multigrid method for solving a wide variety of partial differential equations is now applied to problems in physics, biophysics and engineering. Since algorithms emerging in one discipline are often adapted to problems in other disciplines, it is important to propagate such research results throughout the community.
Computational scientists can now address problems that could not be solved one or two decades ago, and so computational science has emerged as a powerful and indispensable method of analyzing a variety of problems in research, product and process development, and other aspects of manufacturing. Computational inquiry, in the form of numeric simulation, complements theory and experimentation in engineering and scientific research.
Numeric simulations fill the gap between physical experiments and analytical approaches. Numeric simulations provide both qualitative and quantitative insights into many phenomena that are too complex to be dealt with by analytical methods and too expensive or dangerous to study via experiments. Some studies, such as nuclear repository integrity and global climate change, involve time scales that preclude the use of realistic physical experiments. Additional support for numeric simulation stems from the increasing frequency with which simulations are providing results of comparable accuracy to physical experiments.