What is scientific modeling?

Scientific modeling involves creating representations of real-world phenomena that are hard to observe directly. These models can be physical, conceptual, or mathematical and are used across scientific disciplines to explain and predict behaviors of objects or systems. While models are central to modern science, they are, at best, approximations, not exact replicas, which means scientists are always working to improve them. Models can visualize objects (like the DNA double helix) or describe abstract phenomena (like weather forecasting). Predictive models are valuable for warning systems but can be unreliable due to necessary assumptions. Multiple models representing different parts of a system may be needed for a more complete understanding, such as the wave and particle models of light.

Why do scientists use models in their work?

Scientists use scientific modeling to explain and predict the behavior of real objects or systems that are difficult to observe directly. Models can help visualize objects or systems using experimental data or describe abstract or hypothetical behaviors. Predictive models play a significant role in warning systems for large-scale disasters like earthquakes and epidemics. Models can also formulate problems and explore their structure to reveal possible courses of action. However, scientific models are approximations rather than exact replicas, so scientists are continuously working to refine them.

What are some examples of scientific models?

Scientific models are representations of real phenomena that are difficult to observe directly. They explain and predict the behavior of real objects or systems. Examples include: * DNA model A three-dimensional double-helix model of DNA is used to visualize the structure. * Weather forecasting models Predictive models are employed in weather forecasting to project health outcomes of disease epidemics, generally based on knowledge and data of phenomena from the past, relying on mathematical analyses of this information to forecast future, hypothetical occurrences of similar phenomena. * Bohr atomic model Describes the structure of atoms, incorporating quantum theory and serving as a basic conceptual model of electron orbits. * General circulation model Used for simulating human and non-human-induced climate change. * Mathematical models Anything in the physical or biological world can be analyzed by mathematical models if it can be described in terms of mathematical expressions. * Physical models Physical (material) models, such as Drosophila flies and the nematode Caenorhabditis elegans, are used to investigate the functions of genes and proteins. * Protein models Three-dimensional models of proteins are used to gain insight into protein function and to assist with drug design.

How can models help scientists make predictions?

Scientific models are representations of real phenomena that are difficult to observe directly. They help explain and predict the behavior of real objects or systems across various scientific disciplines. Predictive models, for example, are based on past data and mathematical analyses to forecast future events, valuable for warning systems like those for earthquakes and epidemics. Climate models can simulate human- and non-human-induced climate change, advancing knowledge of volcanoes, earthquakes, and Earth's surface evolution. However, models are approximations, not exact replicas, and rely on assumptions that can affect reliability and lead to incorrect conclusions. Multiple models, each representing a part of a system, may be needed for a more complete understanding, such as the wave and particle models of light.

What is the difference between physical, mathematical, and computer models?

Scientific modeling involves creating representations of real phenomena for explanation and prediction in various disciplines. These models can be physical, mathematical, or computer-based. Physical models are tangible representations of mathematical concepts, such as geometric figures. Mathematical models use mathematical expressions to analyze physical or biological systems, like industrial processes or traffic patterns. Computer models simulate the dynamic responses of systems using mathematical descriptions and computer programs, useful for studying complex scenarios like weather patterns or engineering structures. The purpose of scientific modeling can vary from visualizing an object or system, to describing abstract or hypothetical behavior or phenomena. Collectively the models may provide a more complete representation, or at least a more complete understanding, of the real object or system.

How do scientists test and improve their models over time?

Scientists test and improve their models through continuous refinement. The scientific method involves developing a hypothesis, testing it via experiments, and modifying it based on the outcomes. Models may be deficient due to irrelevant variables, exclusion of relevant variables, inaccurate variable evaluation, incorrect structure, or incorrectly formulated constraints. To test a model, scientists compare values computed from the model with actual values under different conditions. Significant differences or variability indicate the model requires repair. This testing avoids using data already used to construct the model to ensure it predicts performance accurately. Peer review also plays a crucial role, with experts providing feedback to improve research proposals and papers.

Scientific modeling

Introduction References & Edit History Related Topics

Images

A conceptual diagram shows a 3D grid box representing the atmosphere and Earth's surface, illustrating climate modeling components such as solar radiation, emitted and reflected radiation, momentum from winds and currents, heat transfer, water vapor, and features like mountains, land, ocean, and weather systems, all linked to a globe below.

Diagram of the Bohr atomic model showing an electron moving in circular orbits with quantized energy levels around a central nucleus, alongside an energy level diagram illustrating electron transitions.

Computer-generated model of anthrax toxin showing seven differently colored protein units, with a small gold molecule representing a drug bound to the protein structure.

Quick Facts

Key People:: Stephen Wolfram

Related Topics:: atomic model; economic model; symbolic model; chondritic model; General Problem Solver

See all related content

Scientific modeling

science

Written by

Kara Rogers

Fact-checked by

Britannica Editors

Last updated

Nov. 12, 2026 •History

Top Questions

What is scientific modeling?
Scientific modeling involves creating representations of real-world phenomena that are hard to observe directly. These models can be physical, conceptual, or mathematical and are used across scientific disciplines to explain and predict behaviors of objects or systems.

While models are central to modern science, they are, at best, approximations, not exact replicas, which means scientists are always working to improve them. Models can visualize objects (like the DNA double helix) or describe abstract phenomena (like weather forecasting). Predictive models are valuable for warning systems but can be unreliable due to necessary assumptions. Multiple models representing different parts of a system may be needed for a more complete understanding, such as the wave and particle models of light.
2 Britannica Sources
1. operations research: Model construction
2. scientific modeling
Created from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles.
Why do scientists use models in their work?
Scientists use scientific modeling to explain and predict the behavior of real objects or systems that are difficult to observe directly. Models can help visualize objects or systems using experimental data or describe abstract or hypothetical behaviors.

Predictive models play a significant role in warning systems for large-scale disasters like earthquakes and epidemics. Models can also formulate problems and explore their structure to reveal possible courses of action.

However, scientific models are approximations rather than exact replicas, so scientists are continuously working to refine them.
2 Britannica Sources
1. operations research: Testing the model and the solution
2. scientific modeling
Created from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles.
What are some examples of scientific models?
Scientific models are representations of real phenomena that are difficult to observe directly. They explain and predict the behavior of real objects or systems.

Examples include:
* DNA model A three-dimensional double-helix model of DNA is used to visualize the structure.
* Weather forecasting models Predictive models are employed in weather forecasting to project health outcomes of disease epidemics, generally based on knowledge and data of phenomena from the past, relying on mathematical analyses of this information to forecast future, hypothetical occurrences of similar phenomena.
* Bohr atomic model Describes the structure of atoms, incorporating quantum theory and serving as a basic conceptual model of electron orbits.
* General circulation model Used for simulating human and non-human-induced climate change.
* Mathematical models Anything in the physical or biological world can be analyzed by mathematical models if it can be described in terms of mathematical expressions.
* Physical models Physical (material) models, such as Drosophila flies and the nematode Caenorhabditis elegans, are used to investigate the functions of genes and proteins.
* Protein models Three-dimensional models of proteins are used to gain insight into protein function and to assist with drug design.
3 Britannica Sources
Created from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles.
How can models help scientists make predictions?
Scientific models are representations of real phenomena that are difficult to observe directly. They help explain and predict the behavior of real objects or systems across various scientific disciplines.

Predictive models, for example, are based on past data and mathematical analyses to forecast future events, valuable for warning systems like those for earthquakes and epidemics. Climate models can simulate human- and non-human-induced climate change, advancing knowledge of volcanoes, earthquakes, and Earth's surface evolution.

However, models are approximations, not exact replicas, and rely on assumptions that can affect reliability and lead to incorrect conclusions. Multiple models, each representing a part of a system, may be needed for a more complete understanding, such as the wave and particle models of light.
4 Britannica Sources
Created from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles.
What is the difference between physical, mathematical, and computer models?
Scientific modeling involves creating representations of real phenomena for explanation and prediction in various disciplines. These models can be physical, mathematical, or computer-based.
- Physical models are tangible representations of mathematical concepts, such as geometric figures.
- Mathematical models use mathematical expressions to analyze physical or biological systems, like industrial processes or traffic patterns.
- Computer models simulate the dynamic responses of systems using mathematical descriptions and computer programs, useful for studying complex scenarios like weather patterns or engineering structures.
The purpose of scientific modeling can vary from visualizing an object or system, to describing abstract or hypothetical behavior or phenomena. Collectively the models may provide a more complete representation, or at least a more complete understanding, of the real object or system.
4 Britannica Sources
Created from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles.
How do scientists test and improve their models over time?
Scientists test and improve their models through continuous refinement. The scientific method involves developing a hypothesis, testing it via experiments, and modifying it based on the outcomes.

Models may be deficient due to irrelevant variables, exclusion of relevant variables, inaccurate variable evaluation, incorrect structure, or incorrectly formulated constraints.

To test a model, scientists compare values computed from the model with actual values under different conditions. Significant differences or variability indicate the model requires repair. This testing avoids using data already used to construct the model to ensure it predicts performance accurately. Peer review also plays a crucial role, with experts providing feedback to improve research proposals and papers.
5 Britannica Sources
Created from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles.

Scientific modeling, the generation of a physical, conceptual, or mathematical representation of a real phenomenon that is difficult to observe directly. Scientific models are used to explain and predict the behaviour of real objects or systems and are used in a variety of scientific disciplines, ranging from physics and chemistry to ecology and the Earth sciences. Although modeling is a central component of modern science, scientific models at best are approximations of the objects and systems that they represent—they are not exact replicas. Thus, scientists constantly are working to improve and refine models.

The purpose of scientific modeling varies. Some models, such as the three-dimensional double-helix model of DNA, are used primarily to visualize an object or system, often being created from experimental data. Other models are intended to describe an abstract or hypothetical behaviour or phenomenon. For example, predictive models, such as those employed in weather forecasting or in projecting health outcomes of disease epidemics, generally are based on knowledge and data of phenomena from the past and rely on mathematical analyses of this information to forecast future, hypothetical occurrences of similar phenomena. Predictive models hold significant value for society because of their potential role in warning systems, such as in the case of earthquakes, tsunamis, epidemics, and similar large-scale disasters. However, because no single predictive model can account for all the variables that may affect an outcome, scientists must make assumptions, which can compromise the reliability of a predictive model and lead to incorrect conclusions.

The limitations of scientific modeling are emphasized by the fact that models generally are not complete representations. The Bohr atomic model, for example, describes the structure of atoms. But while it was the first atomic model to incorporate quantum theory and served as a basic conceptual model of electron orbits, it was not an accurate description of the nature of orbiting electrons. Nor was it able to predict the energy levels for atoms with more than one electron.

Scientific modeling

Thank you for signing up!