solving scientific and engineering problems
scientific computing, also known as computational science, uses computational methods to solve science and engineering problems. the modeling of natural systems using numerical simulation is an important area of focus within scientific computing. these models are often computationally intensive and require high-performance computing resources.
scientists and engineers often create models using applied mathematical methods for fourier analysis, numerical linear algebra, and solving ordinary and partial differential equations. models are often implemented using programming languages or domain-specific modeling tools.
most common among these is matlab®, a high-level language and interactive development environment with prebuilt functions for scientific computing. for detail on solving specialized classes of problems, see the toolboxes for statistics, optimization, and parallel computing.
examples and how to
software reference
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see also: random number generation, mathematical modeling, parallel computing, numerical analysis, statistics and machine learning toolbox, optimization toolbox, research with matlab