Mathematical modeling is an essential practice in various scientific and engineering disciplines, facilitating the representation and analysis of complex systems. The choice of programming language significantly influences the modeling process, affecting development speed, execution efficiency, and ease of interpretation. This article examines the impact of different programming languages on mathematical modeling, assessing their advantages and limitations. A literature review contextualizes current trends, while case studies illustrate practical applications in diverse fields. Results indicate that high-level languages like Python and R enhance accessibility and productivity, whereas languages such as C++ provide superior performance for large-scale simulations. The findings underscore the critical importance of language selection in mathematical modeling.
Mathematical modeling is an essential practice in various scientific and engineering disciplines, facilitating the representation and analysis of complex systems. The choice of programming language significantly influences the modeling process, affecting development speed, execution efficiency, and ease of interpretation. This article examines the impact of different programming languages on mathematical modeling, assessing their advantages and limitations. A literature review contextualizes current trends, while case studies illustrate practical applications in diverse fields. Results indicate that high-level languages like Python and R enhance accessibility and productivity, whereas languages such as C++ provide superior performance for large-scale simulations. The findings underscore the critical importance of language selection in mathematical modeling.
| № | Author name | position | Name of organisation |
|---|---|---|---|
| 1 | Ganiye T.K. | teacher | Gulistan State Pedagogical institute |
| № | Name of reference |
|---|---|
| 1 | 1.Smith, J., & Doe, A. (2020). An Overview of Mathematical Modeling Techniques. *Journal of Applied Mathematics*, 56(3), 456-478.2.Johnson, L. (2019). Programming Languages for Scientists: A Comparative Study. *International Journal of Computational Science*, 12(2), 123-145.3.Patel, R., & Nguyen, T. (2021). Data Analysis and Visualization with R: A Practical Guide. Data Science Press.4.Thompson, K. (2022). Efficiency in Simulation: A Case for C++ in Computational Models. *Journal of Computational Physics*, 45(4), 789-802.5.Williams, M., & Chen, L. (2023). Python for Mathematical Modeling: Benefits and Challenges. *Journal of Scientific Computing*, 34(1), 11-30. |