Scientific computing refers to the set of tools, theories and techniques which are needed for solving complex scientific and engineering problems on a computer. It acts like the “third pillar” in scientific discovery, just beside experimentation and theoretical analysis. Most of these scientific computing tools had been developed originally in Mathematics. So, many of these have actually had their origin long before electronic computers were discovered. These mathematical tools and theories are part of Numerical Analysis or Numerical Mathematics. This is an integral aspect of scientific computing. When the modern day electronic computers came about, this brought in a new age of solutions for the scientific problems.
In scientific computing, there is an application of high-end numerical algorithms in solving large scale computing problems in engineering and science. Numerical analysis refers to methods which specifically deal with how such methods are developed and their accuracy and complexity. This makes it a specialized field of study. Scientific computing is needed when problems at hand fail to be resolved by theories and traditional experiments. For instance, an attempt to forecast climatic changes is difficult without scientific computing tools.
Other areas where scientific computing may become necessary is in areas where carrying out experiments is difficult or dangerous. Scientific computing is also the best alternative when the problem at hand is likely to be very time consuming and costly; for example, determining the protein structure. Finally, scientific computing may be required for creating algorithms for optimal designs, like in designing aircrafts where scientific computing is more useful than designs through repeated trial and error. So, scientific computing turns out to be a multi-disciplinary activity which will involve efforts from a variety of experts like application professionals, applied mathematicians and even computer scientists.
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