# Computational Model

A Computational Model is a mathematical model that can be developed into algorithm to simulate and study a set of processes observed in a system.

**Example(s):****Counter-Example(s):****See:**Neural Network, Mathematical Model, Computational Science, Computational Resource, Complex System, Computer Simulation, Nonlinear System, Analytical Solution, Weather Forecasting, Earth Simulator, Flight Simulator, Protein Folding.

## References

### 2018a

- (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Computational_model Retrieved:2018-9-2.
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**computational model**is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation.The system under study is often a complex nonlinear system for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by adjusting the parameters of the system in the computer, and studying the differences in the outcome of the experiments. Operation theories of the model can be derived/deduced from these computational experiments.

Examples of common computational models are weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, and neural network models.

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### 2018b

- (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Computer_simulation#Simulation_versus_model Retrieved:2018-9-2.
- A computer model is the algorithms and equations used to capture the behavior of the system being modeled. By contrast, computer simulation is the actual running of the program that contains these equations or algorithms. Simulation, therefore, is the process of running a model. Thus one would not "build a simulation"; instead, one would "build a model", and then either "run the model" or equivalently "run a simulation".

### 2018c

- (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Mathematical_model#Training_and_tuning Retrieved:2018-9-2.
- Any model which is not pure white-box contains some parameters that can be used to fit the model to the system it is intended to describe. If the modeling is done by an artificial neural network or other machine learning, the optimization of parameters is called
*training*, while the optimization of model hyperparameters is called*tuning*and often uses cross-validation. In more conventional modeling through explicitly given mathematical functions, parameters are often determined by*curve fitting*.

- Any model which is not pure white-box contains some parameters that can be used to fit the model to the system it is intended to describe. If the modeling is done by an artificial neural network or other machine learning, the optimization of parameters is called