Model classification refers to the process of classifying, planning, and managing different kinds of models. There are several types of models related to business processes and related modeling languages to deal with various aspects of an organization’s processes. This article provides a taxonomy of modeling types and highlights how each model type needs to interact with others to support broader modeling practices. The article also provides a short checklist of modeling techniques to avoid common modeling mistakes.
The basic models include geometric models and the operational models such as mechanical, structural, and electrical models. geometric models are geometrical representations or graphical models which can be in a formulation that is formulated by a set of axioms. An example of a geometric model is a set of surface shapes on a Cartesian surface. The set of axioms is then used to define other geometrical models.
Structural models are numerical representations of structures. These may represent actual physical structures or abstract theoretical structures (such as tetric equations) that may be used to describe or predict physical behaviors. The main advantage of using structural models in the context of science is that they are more concrete and therefore more testable. On the other hand, there are also some objections to the use of structural models in the context of the real world.
System models are designed to approximate a state of any physical system. For instance, a model may include the physical processes necessary for the behavior of an elementary particle, such as the proton, electron, or quark. In scientific language, this is called a “state-transition model.” A state-transition model can be thought of as a formulation in which a constant set of initial conditions (e.g., initial temperature, pressure, gravity) on the system’s various parts are allowed to vary in time according to some initial chosen parameters. In some cases, the model may include energy levels as well.
Complex models are statistical formulations, which make generalizations about certain real properties of many physical processes. For instance, a baseball hitting machine may make predictions about the effectiveness of different pitches in striking batters. Other examples include complex systems representing global warming. Most researchers who study the real world focus their research efforts on models, as they enable researchers to make predictions about the effects of climate change.
Many recent advances in mathematics have resulted in the development of more sophisticated system models. For instance, in order to deal with elliptical orbit calculation, a group at the Massachusetts Institute of Technology developed a generalized coordinate system modeling framework. Complex systems modeling is now a branch of mathematics known as lattice models, which are used in chemical synthesis and in the nuclear power industry. Another important development in recent years has been the development of tensors, which allow for the indefinite description of physical systems at higher dimension levels. The future of mathematical modeling lies in such areas as bioinformatics, which are concerned with genetic architecture and function, and artificial intelligence, which aim to develop computers that can understand, reason, and compute, among other things.