Scientific modeling is a scientific activity, whose purpose is to make certain parts or features of the world easier to understand, define, measure, visualize, or simulate by referring to existing and generally accepted knowledge. It takes selection and identification of relevant aspects of the real-world situation and then uses different types of models for different purposes, such as conceptual models for better understanding, operational models for operationalizing, mathematical models for measuring, and graphical models to visualize the subject. Modeling is an important and inseparable part of many disciplines, each of which has their own ideas about a particular type of modeling.
There is also increasing attention to scientific modeling in such fields as science education, philosophy of science, system theory, and visualization of knowledge. There is a growing collection of methods, techniques and meta-theories about all kinds of specialized scientific modeling.
Video Scientific modelling
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The scientific model seeks to represent the empirical objects, phenomena, and physical processes in a logical and objective way. All models are in simulacra , that is, a simple reflection of the fact that, although there are estimates, can be very useful. Building and debating models is fundamental to scientific companies. Complete and correct representation may not be possible, but scientific debates are often a concern that is a better model for a given task, for example, which is a more accurate climate model for seasonal forecasting.
Attempts to formalize the principles of empirical science use interpretation to model reality, in the same way logicians do axiomatic to the principles of logic. The purpose of this effort is to establish a formal system that will not produce theoretical consequences that are contrary to what is found in reality. Predictions or other statements taken from such formal system mirrors or mapped the real world only to the extent that this scientific model is true.
For scientists, the model is also the way in which the process of human thought can be strengthened. For example, the model provided in the software allows scientists to harness the power of computing to simulate, visualize, manipulate and gain intuition about the entity, phenomenon, or process that it represents. Such a computer model is in silico . Another type of scientific model is in vivo (life model, such as laboratory mice) and in vitro (in glass, such as tissue culture).
Maps Scientific modelling
Fundamentals of scientific modeling
Model in place of direct measurements and experiments
Models are usually used when it is impossible or impractical to create experimental conditions in which scientists can directly measure results. Direct measurement of results under controlled conditions (see Scientific method) will always be more reliable than the estimates of the results being modeled.
In modeling and simulation, a model is a simplification done by task, deliberate simplification, and the abstraction of reality perception, formed by physical, legal, and cognitive constraints. It is task-driven, because the model is captured with specific questions or tasks in mind. Simplification leaves behind all known and observed entities and their unimportant relationships to the task. Abstraction collects important information, but is not required in the same detail as the desired object. Both activities, simplification and abstraction, are done intentionally. However, they are based on the perception of reality. This perception has become the model in itself, because it is equipped with physical constraints. There are also obstacles to what we can legally observe with our current tools and methods, and the cognitive constraints that limit what we can explain with our current theory. This model consists of concepts, their behavior, and their relationship in a formal form and is often referred to as a conceptual model. To run the model, it needs to be implemented as a computer simulation. This requires more choices, such as numerical estimates or heuristic usage. Apart from all these epistemological and computational constraints, simulations have been recognized as the third pillar of the scientific method: the development of theory, simulation, and experimentation.
Simulation
Simulation is a model implementation. Steady state simulations provide information about the system at any given time (usually at equilibrium, if such a state exists). Dynamic simulations provide information over time. A simulation brings the model to life and shows how a particular object or phenomenon will behave. Such simulations can be useful for testing, analysis, or training in cases where real-world systems or concepts can be represented by the model.
Structure
Structure is a fundamental and sometimes intangible notion that includes recognition, observation, nature, and stability patterns and entity relationships. From the child's verbal description of snowflakes, to detailed scientific analysis of the properties of the magnetic field, the concept of structure is an important foundation of almost every mode of investigation and discovery in science, philosophy, and art.
System
A system is a set of interacting or interdependent, real or abstract entities, forming an integrated whole. In general, a system is a construction or a collection of different elements that can together produce results that can not be obtained by the elements themselves. The concept of 'integrated whole' can also be expressed in terms of systems that embody a set of relationships that are distinguished from relations from the set to other elements, and from the relationship between elements of the set and elements not part of the relational regime. There are two types of system models: 1) discrete in which variables change instantly at separate points in time and, 2) continuous where the state variables change constantly over time.
Generate model
Modeling is the process of producing the model as a conceptual representation of some phenomena. Normally the model will only deal with some aspects of the phenomenon, and the two models of the same phenomenon may be fundamentally different - that is, that the difference between them consists more than just a simple renaming of a component.
These differences may be due to different requirements of the model end-users, or the conceptual or aesthetic differences between modelers and contingent decisions made during the modeling process. Considerations that may affect the model structure may be the modeller's preference for reducing ontology, preferences on statistical models versus deterministic models, discrete versus continuous time, etc. However, the model user needs to understand the assumptions made related to his validity for a particular use.
Building a model requires an abstraction. Assumptions are used in modeling to define model implementation domains. For example, the special theory of relativity assumes an inertial frame of reference. This assumption is contextualized and further elaborated by the general theory of relativity. A model makes accurate predictions when its assumptions are valid, and may not make accurate predictions when the assumptions do not apply. Such assumptions are often the point at which older theories are replaced by new theories (the general theory of relativity works in non-inertial reference frames as well).
The term "assumption" is actually broader than the standard use, etymologically. The Oxford English Dictionary (OED) and Wiktionary online indicate its Latin source as assumere ("accept, to take it upon yourself, adopt, seize"), which is a composite of ad - ("to, towards, at") and sumere (to fetch). Roots survive, with meaning shifting, in Italian sumere and Spanish sumir . In the OED, "assume" has the feeling of (i) "investing in (attributes)," (ii) "to do" (especially in the Act), (iii) "only for oneself in appearance, to ostensibly own, "and (iv)" to think something is happening. "Thus," assumption "connotes another association rather than the notion of contemporary" what is assumed or taken for granted, a presupposition, a postulate, "and deserves a broader analysis of philosophy of science.
Evaluate model
A model is evaluated first and foremost by its consistency with empirical data; any model inconsistent with reproducible observations should be modified or rejected. One way to modify a model is to limit the domain in which it is credited with having high validity. The example of the case is Newtonian physics, which is very useful except for the very small, very fast, and very large phenomena of the universe. However, matches for empirical data alone are not sufficient for the model to be accepted as valid. Other important factors in evaluating a model include:
- Ability to explain previous observations
- Ability to predict future observations
- Usage charges, especially in combination with other models
- Refutability, allowing an estimate of the confidence level of the model
- Simplicity, or even aesthetic appeal
One might try to measure model evaluation using the utility function.
Visualization
Visualization is any technique for creating images, diagrams, or animations to communicate messages. Visualization through visual imagery has been an effective way to communicate abstract and concrete ideas since the dawn of man. Examples from history include cave paintings, Egyptian hieroglyphs, Greek geometry, and Leonardo da Vinci's revolutionary method of drawing techniques for technical and scientific purposes.
Space mapping
Spatial mapping refers to a methodology that uses a "quasi-global" modeling formulation to associate "rough" (ideal or low-fidelity) companions with "fine" (practical or high-quality) models with different complexities. In engineering optimization, space mapping juxtaposes (maps) a very rough model quickly with the correspondingly expensive cost-to-hit model so as to avoid the direct optimization of expensive fines models. The recurring alignment filters the crude model "mapped" (alternate model).
Scientific modeling type
Apps
Modeling and simulation
One of the applications of scientific modeling is the field of modeling and simulation, commonly referred to as "M & S". M & amp; S has an application spectrum that ranges from conceptual development and analysis, through experimentation, measurement and verification, to disposal analysis. Projects and programs can use hundreds of different simulations, simulators, and model analysis tools.
The image shows how Modeling and Simulation are used as a central part of an integrated program in the defense capability development process.
Model-based learning in education
Model-based learning in education, particularly in relation to science involves students making models for scientific concepts to:
- Get insights into the scientific idea (s)
- Get a deeper understanding of the subject through model visualization
- Increase student engagement in the course
Different types of model-based learning techniques include:
- The physical macrocosm
- Representative system
- Syntactic model
- New model
Modeling in education is a recurring practice with students refining, developing and evaluating their models over time. It transforms learning from the stiffness and monotony of the traditional curriculum into an exercise of student creativity and curiosity. This approach uses a constructive strategy of social collaboration and learning scaffold theory. Model-based learning includes cognitive reasoning skills in which existing models can be improved by building new models using the old model as the basis.
"Model-based learning requires the determination of target models and learning pathways that provide realistic opportunities for understanding." The modeling can also incorporate integrated learning strategies using web-based tools and simulators, allowing students to:
- Familiarize yourself with on-line or digital resources
- Create different models with virtual materials with little or no cost
- Practice modeling activities anytime and anywhere
- Filter existing models
"A well-designed simulation simplifies real-world systems while raising awareness of the complexity of the system Students can participate in simplified systems and learn how the actual systems operate without spending the days, weeks or years required to live this experience in the real world."
The role of teachers in the whole process of teaching and learning is primarily a facilitator and organizer of learning experiences. He will assign students, model activities for specific concepts and provide relevant information or support for the activity. For virtual modeling activities, teachers can also provide information about the use of digital tools and make problem-solving support in case of interruption while using the same. Teachers can also organize group discussion activities between students and provide the platform necessary for students to share their observations and knowledge taken from modeling activities.
Evaluation of model-based learning may include the use of rubrics that assess students' ingenuity and creativity in model construction as well as the overall class participation of students vis-a-vis knowledge built through the activity.
However, it is important to consider the following for successful model-based learning to occur:
- Use of the right tool at the right time for a particular concept
- Provision in educational preparation for modeling activities: eg, computer room with internet facility or installed software to access simulators or digital devices
See also
- Heuristics
- Scientific visualization
- Statistical model
References
Further reading
There are currently about 40 magazines on scientific modeling that offer all kinds of international forums. Since the 1960s there have been a number of books and magazines that thrive on specific forms of scientific modeling. There is also much discussion of scientific modeling in philosophy-science literature. A choice:
- Rainer Hegselmann, Ulrich MÃÆ'¼ller and Klaus Troitzsch (eds.) (1996). Modeling and Simulation in Social Sciences from Philosophy of Science of Viewpoint. Theory and Decision Library . Dordrecht: Kluwer.
- Paul Humphreys (2004). Extend Yourself: Computational Science, Empiricism, and Scientific Method . Oxford: Oxford University Press.
- Johannes Lenhard, GÃÆ'¼nter KÃÆ'¼ppers and Terry Shinn (Eds.) (2006) "Simulation: Pragmatic Constructions of Reality", Springer Berlin.
- Tom Ritchey (2012). "Outline for Morphology Modeling Method: Contribution to General Modeling Theory". In: Acta Morphologica Generalis , Vol 1. No. 1. pp.Ã, 1-20.
- Fritz Rohrlich (1990). "Computer Simulation in Physics Science". In: Proceedings of the Philosophy of Science Association, Vol. 2 , edited by Arthur Fine et al., 507-518. East Lansing: The Philosophy of Science Association.
- Rainer Schnell (1990). "Computersimulation und Theoriebildung in den Sozialwissenschaften". In: KÃÆ'¶lner Zeitschrift fÃÆ'¼r Soziologie und Sozialpsychologie 1, 109-128.
- William Silvert (2001). "Modeling as a Discipline". In: Int. A. General Systems. Vol. 30 (3), pp. 261.
- Sergio Sismondo and Snait Gissis (eds.) (1999). Modeling and Simulation. Special Edition of Science in Context 12.
- Eric Winsberg (2001). "Simulations, Models, and Theories: Complex Physical Systems and Their Representation". In: Philosophy of Science 68 (Proceedings): 442-454.
- Eric Winsberg (2003). "Experimental Simulation: Methodology for the Virtual World". In: Philosophy of Science 70: 105-125.
- TomÃÆ'¡? Helikar, Jim A Rogers (2009). "ChemChains: platform for simulation and analysis of biochemical networks devoted to laboratory scientists". BioMed Center.
External links
- Models. Sign in in Encyclopedia of Internet Philosophy
- Models in Science. Login in Stanford Encyclopedia of Philosophy
- The World as a Process: Simulations in Nature and Social Sciences, in: R. Hegselmann et al. (eds.), Modeling and Simulation in Social Sciences from Philosophy of Science of Viewpoint, Theory and Library Decisions. Dordrecht: Kluwer 1996, 77-100.
- Research in simulation and modeling of various physical systems
- Model the Water Quality Information Center, US Department of Agriculture
- Ecotoxicology & amp; Model
- Morphology of Modeling Methods. Acta Morphologica Generalis, Vol 1. No. 1. pp.Ã, 1-20.
Source of the article : Wikipedia
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