The scientific model is an abstract representation of phenomena and processes to explain them. A scientific model is a visual representation of the solar system in which the relationship between planets, the Sun and movements is appreciated .
Through the introduction of data in the model allows to study the final result. To make a model it is necessary to raise certain hypotheses, so that the representation of the result we want to obtain is as exact as possible, as well as simple so that it is easily manipulated.
There are several types of methods, techniques and theories for shaping scientific models. And in practice, each branch of science has its own method for making scientific models, although you can include models from other branches to verify your explanation.
The principles of modeling allow the creation of models according to the branch of science that they try to explain. The way to build analysis models is studied in the philosophy of science, general systems theory, and scientific visualization.
In almost all explanations of phenomena, one model or another can be applied, but it is necessary to adjust the model to be used, so that the result is as accurate as possible. Maybe you are interested the 6 steps of the scientific method and what they consist of .
General parts of a scientific model
To create a model, a series of data and an organization of the same are needed. From a set of input data, the model will provide a series of output data with the result of the hypotheses raised
The internal structure of each model will depend on the type of model that we are proposing. Normally, it defines the correspondence between the input and the output.
The models can be deterministic when each input corresponds to the same output, or also non-deterministic, when different outputs correspond to the same input.
Types of models
The models are distinguished by the form of representation of their internal structure. And from there we can establish a classification.
Within the physical models we can distinguish between theoretical and practical models. The most widely used practical model types are mockups and prototypes.
They are a representation or copy of the object or phenomenon to be studied, which makes it possible to study their behavior in different situations.
It is not necessary for this representation of the phenomenon to be carried out at the same scale, but rather they are designed in such a way that the resulting data can be extrapolated to the original phenomenon based on its size.
In the case of theoretical physical models, they are considered models when the internal dynamics is not known.
Through these models it is sought to reproduce the phenomenon studied, but not knowing how to reproduce it, hypotheses and variables are included to try to explain why this result is obtained. It is applied in all variants of physics, except in theoretical physics.
Within mathematical models it is sought to represent the phenomena through a mathematical formulation. This term is also used to refer to geometric models in design. They can be divided into other models.
The deterministic model is one in which it is assumed that the data are known, and that the mathematical formulas used are exact to determine the result at any time, within the observable limits.
Stochastic or probabilistic models are those in which the result is not exact, but rather a probability. And in which there is an uncertainty as to whether the approach of the model is correct.
Numerical models, on the other hand, are those that through numerical sets represent the initial conditions of the model. These models are what allow simulations of the model by changing the initial data to know how the model would behave if it had other data.
In general, mathematical models can also be classified depending on the type of inputs with which one works. They can be heuristic models where explanations of the cause of the phenomenon that is being observed are sought.
Or they can be empirical models, where the results of the model are checked through the outputs obtained from the observation.
And finally, they can also be classified according to the objective they want to achieve. They can be simulation models where one tries to predict the results of the phenomenon that is being observed.
They can be optimization models, in these the operation of the model is considered and an attempt is made to find the point that can be improved to optimize the result of the phenomenon.
Finally, they can be control models, where they try to control the variables to control the result obtained and to be able to modify it if necessary.
Through graphic resources a data representation is made. These models are normally lines or vectors. These models facilitate the vision of the phenomenon represented through tables and graphs.
It is the material representation of an object or process. It is used to validate certain hypotheses that would otherwise be impossible to test. This model is successful when it is possible to provoke the same phenomenon that we are observing, in its analogue
They are maps of abstract concepts that represent the phenomena to be studied, including assumptions that allow a glimpse of the result of the model and can be adjusted to it.
They have a high level of abstraction to explain the model. They are the scientific models per se, where the conceptual representation of the processes manage to explain the phenomenon to be observed.
Representation of models
The factors of the model are measured through an organization of the qualitative descriptions of the variables to be studied within the model.
Through a mathematical formulation the representation models are established. It is not necessary that they be numbers, but the mathematical representation can be algebraic or mathematical graphs
When prototypes or models are established that try to reproduce the phenomenon to be studied. In general they are used to reduce the scale necessary for the reproduction of the phenomenon that is being studied.
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