Scientific Computing
Computational methods and tools to solve complex scientific and engineering problems.
Scientific computing is an interdisciplinary field that utilizes advanced computing capabilities to understand and solve complex problems in science and engineering. This involves the development and application of mathematical models, numerical algorithms, and the use of computer simulations to analyze and predict the behavior of natural and engineered systems. It encompasses areas such as computational physics, chemistry, biology, and engineering, and often requires high-performance computing (HPC) resources to handle large-scale computations. The field is critical for tasks like climate modeling, computational fluid dynamics, molecular modeling, and the analysis of large data sets from experiments and simulations.
The term "scientific computing" emerged in the mid-20th century, paralleling the development of electronic computers in the 1940s and 1950s. It gained significant traction in the 1970s and 1980s with the advent of more powerful computers and the development of numerical methods and algorithms that could solve increasingly complex problems.
Key figures in the development of scientific computing include John von Neumann, who was instrumental in the development of early computers and computational methods, and scientists like Donald Knuth, known for his work on algorithms and computational theory. The establishment of institutions like the National Center for Supercomputing Applications (NCSA) and the development of software such as MATLAB and ANSYS have also been pivotal in advancing the field.