1. Mathematical model:
Linear programming model: use linear programming to analyze and solve problems, especially in optimization problems.
Difference equation model: used to describe the dynamic process of the system changing with time, such as population growth and species competition.
Probability model: used to deal with random problems, including Bayesian model, Markov chain, etc.
Statistical models: such as regression analysis, variance analysis, etc. , which is used to analyze the relationship and changing trend between data.
Differential equation model: used to describe the rate of change, such as population growth and chemical reaction rate in the ecosystem.
2. Scientific model:
Earth climate model: used to predict climate change trends, weather patterns, etc. To deal with environmental problems.
Ecosystem model: describing the interaction and influence between various organisms in the ecosystem is helpful to environmental protection and ecological balance.
3. Management mode:
Supply chain model: used to optimize the supply chain between production and sales, improve efficiency and reduce costs.
Decision tree model: used to assist decision-making, analyze various options and help make the best decision.
Risk management model: used to analyze, evaluate and reduce risks, involving investment, finance, enterprise management and other fields.
4. Engineering model:
Structural mechanics model: used to predict the deformation and failure of structures under the action of external forces and guide engineering design.
Circuit model: It is helpful to design and optimize the circuit by describing the relationship between current and voltage among components in the circuit.
Fluid mechanics model: describes the flow characteristics of fluid in different environments, including water flow and air flow.
5. Computer science model:
Neural network model: imitates the structure and working principle of human nervous system and is used for image recognition and prediction.
Machine learning model: Train the machine through a large amount of data, so that the machine can learn independently and adapt to the new situation.
6. Social science model:
Economic model: used to study economic phenomena and laws, including supply and demand, price changes, etc.
Social network model: describing the connection and interaction in interpersonal network is helpful to understand social dynamics and information dissemination.
7. Medical model:
Disease transmission model: used to predict the spread trend and control strategy of diseases in the population.
Drug metabolism model: describes the metabolic process and mechanism of drugs in human body.
Pathological model: used to study the pathogenesis and treatment of diseases and promote the progress of medical science.