Mathematical Modeling: An Introduction to Applied Mathematics


Mathematics can be divided into two areas - pure and applied. In pure mathematics, mathematicians study and develop concepts and theories for their own sake, without necessarily having any practical applications. Applied mathematics, on the other hand, involves using mathematical principles and techniques to solve real-world problems. One of the most important tools used in applied mathematics is mathematical modeling.

What is Mathematical Modeling?

Mathematical modeling is the process of using mathematics to describe and analyze real-world phenomena. It involves creating a mathematical model, which is a simplified representation of a complex system, using equations or other mathematical tools. The model can then be used to make predictions and analyze the behavior of the system under different conditions.

The Modeling Process

The modeling process involves several steps:

  • Identifying the Problem: The first step is to identify the real-world problem that needs to be solved. This could be anything from predicting the spread of a disease to optimizing a manufacturing process.
  • Formulating the Model: Once the problem has been identified, the next step is to create a mathematical model that represents the system. This involves selecting the relevant variables and writing equations or other mathematical expressions that describe the behavior of the system.
  • Solving the Model: Once the model has been formulated, the next step is to solve it. This could involve using analytical techniques, such as solving differential equations, or numerical methods, such as using a computer simulation.
  • Interpreting the Results: The final step is to interpret the results of the model. This involves analyzing the output of the model and making predictions about how the system will behave under different conditions.

Applications of Mathematical Modeling

Mathematical modeling has many applications in science, engineering, and other fields. Some common examples include:

  • Climate Modeling: Climate models are used to predict changes in the Earth's climate due to human activities, such as greenhouse gas emissions.
  • Financial Modeling: Financial models are used to predict stock prices, interest rates, and other economic factors.
  • Biological Modeling: Biological models are used to study the behavior of cells, tissues, and organisms, and to predict the spread of diseases.
  • Engineering Modeling: Engineering models are used to design and optimize systems such as airplanes, bridges, and manufacturing processes.

Challenges in Mathematical Modeling

While mathematical modeling can be a powerful tool for solving real-world problems, it can also be challenging. Some of the main challenges include:

  • Complexity: Real-world systems can be extremely complex, and it can be difficult to capture all of the relevant factors in a mathematical model.
  • Uncertainty: Many real-world systems involve uncertainty, which can be difficult to incorporate into a mathematical model.
  • Data: Mathematical models rely on data to inform their predictions, and it can be difficult to obtain accurate and representative data.
  • Validation: Mathematical models must be validated to ensure that they are accurate and representative of the real-world system.


Mathematical modeling is a powerful tool for solving real-world problems in a wide range of fields. It involves creating a simplified representation of a complex system using mathematical equations and other tools. While mathematical modeling can be challenging, it has many important applications and can help us to better understand the world around us.