Computer simulation developed at the same time as the rapid progress of the computer. Its first large-scale deployment was in the Manhattan Project, during World War II, to recreate a nuclear detonation. The Monte Carlo Method was used. Computer simulations sometimes complement or even replace modeling systems for which closed-form analytical solutions cannot be found. There are many types of computer simulation, but they all share a common characteristic: they try to generate a sample of representative scenarios for a model in which a complete list of all possible states of the model would be very expensive or impossible. Computerized models were initially used as a supplement to other parameters, but later their use was extended to other areas.

Data input and output for the simulation can be done via formatted text documents or a pre- and post-simulation processor.

Data preparation is probably the most important part of computer simulation. Because the simulation is digital, with the inevitable errors of rounding or truncating figures, even the smallest error in the original data can turn into a serious error later in the simulation. Although all computer analyzes are subject to GIGO (false input/false output), it occurs especially in computer simulation. In fact, the discovery of this cumulative and inevitable error in digital systems is the origin of chaos theory.

Computational models can be classified based on different pairs of attributes, namely:

The equations define the relationships between the elements of the modeled system and try to find a state in which the system is in equilibrium. This class of models is usually used to simulate physical systems, that is, as a simpler model before moving on to dynamic modeling.

Previously, the output data from a computer simulation was presented in a table or matrix, showing the effect that multiple parameter changes had on the data. The use of the matrix format was due to the traditional use of the matrix in mathematical models. However, psychologists noticed that humans perceived changes in the development of situations better if they looked at graphs or even moving images or animations generated from the data, such as those run in computer-generated image animations. Thus, even if subjects could not understand numbers or decipher mathematical formulas, by observing a moving weather graph they could be able to predict certain events much faster than by analyzing tables with rain cloud coordinates. These powerful graphical representations, which went beyond the world of numbers and formulas, sometimes produced data outputs that lacked a coordinate map or time frames, deviating too far from numerical data representations. Today, weather forecast models tend to strike a balance between graphs of moving rain or snow clouds with maps that use numerical coordinates and numerical time frames of events.

Similarly, CGI simulations of CT can recreate how a tumor shrinks or changes over a long period of medical treatment, thus showing the passage of time in a representation of the rotating human head.

Other uses of CGI simulations are being developed to graphically represent large amounts of moving data as changes occur in the simulation run.

CGI simulation example.

Computer simulation developed at the same time as the rapid progress of the computer. Its first large-scale deployment was in the Manhattan Project, during World War II, to recreate a nuclear detonation. The Monte Carlo Method was used. Computer simulations sometimes complement or even replace modeling systems for which closed-form analytical solutions cannot be found. There are many types of computer simulation, but they all share a common characteristic: they try to generate a sample of representative scenarios for a model in which a complete list of all possible states of the model would be very expensive or impossible. Computerized models were initially used as a supplement to other parameters, but later their use was extended to other areas.

Data input and output for the simulation can be done via formatted text documents or a pre- and post-simulation processor.

Data preparation is probably the most important part of computer simulation. Because the simulation is digital, with the inevitable errors of rounding or truncating figures, even the smallest error in the original data can turn into a serious error later in the simulation. Although all computer analyzes are subject to GIGO (false input/false output), it occurs especially in computer simulation. In fact, the discovery of this cumulative and inevitable error in digital systems is the origin of chaos theory.

Computational models can be classified based on different pairs of attributes, namely:

The equations define the relationships between the elements of the modeled system and try to find a state in which the system is in equilibrium. This class of models is usually used to simulate physical systems, that is, as a simpler model before moving on to dynamic modeling.

Previously, the output data from a computer simulation was presented in a table or matrix, showing the effect that multiple parameter changes had on the data. The use of the matrix format was due to the traditional use of the matrix in mathematical models. However, psychologists noticed that humans perceived changes in the development of situations better if they looked at graphs or even moving images or animations generated from the data, such as those run in computer-generated image animations. Thus, even if subjects could not understand numbers or decipher mathematical formulas, by observing a moving weather graph they could be able to predict certain events much faster than by analyzing tables with rain cloud coordinates. These powerful graphical representations, which went beyond the world of numbers and formulas, sometimes produced data outputs that lacked a coordinate map or time frames, deviating too far from numerical data representations. Today, weather forecast models tend to strike a balance between graphs of moving rain or snow clouds with maps that use numerical coordinates and numerical time frames of events.

Similarly, CGI simulations of CT can recreate how a tumor shrinks or changes over a long period of medical treatment, thus showing the passage of time in a representation of the rotating human head.

Other uses of CGI simulations are being developed to graphically represent large amounts of moving data as changes occur in the simulation run.

CGI simulation example.