Computer simulation
It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.
[2] Other examples include a 1-billion-atom model of material deformation;[3] a 2.64-million-atom model of the complex protein-producing organelle of all living organisms, the ribosome, in 2005;[4] a complete simulation of the life cycle of Mycoplasma genitalium in 2012; and the Blue Brain project at EPFL (Switzerland), begun in May 2005 to create the first computer simulation of the entire human brain, right down to the molecular level.
By contrast, computer simulation is the actual running of the program that perform algorithms which solve those equations, often in an approximate manner.
Computer simulation is often used as an adjunct to, or substitute for, modeling systems for which simple closed form analytic solutions are not possible.
For some, the input might be just a few numbers (for example, simulation of a waveform of AC electricity on a wire), while others might require terabytes of information (such as weather and climate models).
However, psychologists and others noted that humans could quickly perceive trends by looking at graphs or even moving-images or motion-pictures generated from the data, as displayed by computer-generated-imagery (CGI) animation.
In social sciences, computer simulation is an integral component of the five angles of analysis fostered by the data percolation methodology,[12] which also includes qualitative and quantitative methods, reviews of the literature (including scholarly), and interviews with experts, and which forms an extension of data triangulation.
Engineers can step through the simulation milliseconds at a time to determine the exact stresses being put upon each section of the prototype.
For example, faster than real-time animations can be useful in visualizing the buildup of queues in the simulation of humans evacuating a building.
Although sometimes ignored in computer simulations, it is very important[editorializing] to perform a sensitivity analysis to ensure that the accuracy of the results is properly understood.
For example, the probabilistic risk analysis of factors determining the success of an oilfield exploration program involves combining samples from a variety of statistical distributions using the Monte Carlo method.
