International Peer-Reviewed JournalOpen AccessISSN 2456-8880
irejournals@gmail.com+91-7433024337

Home / Current Issue / Paper 1722138

1722138 Vol 10 · Issue 1 Download Paper

Mathematical Modeling of Population Growth Using Differential Equations

Lavkush Pandey

Subject area: Science,Engineering and Technology  ·  Area of research: Mathematical Population Modeling

DOI: https://doi.org/10.64388/IREV10I1-1722138

Abstract

Population growth is a central topic in mathematical biology and ecological modeling. This paper analyzes population dynamics using exponential, logistic, and extended nonlinear models. The study formulates first-order differential equations to describe how populations evolve over time under different environmental constraints. Analytical solutions are derived for classical models, while advanced extensions such as harvesting effects, Allee thresholds, time-dependent growth rates, delayed feedback systems, and predator–prey interactions are also introduced. Results show that exponential models describe ideal conditions, while logistic and nonlinear models provide realistic and stable long-term population behavior.

Keywords

Logistic Growth, Exponential Growth, Carrying Capacity, Stability Analysis, Ecological Systems, Population Biology.

How to cite this paper

Lavkush Pandey "Mathematical Modeling of Population Growth Using Differential Equations" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026, doi: https://doi.org/10.64388/IREV10I1-1722138
Lavkush Pandey (2026). Mathematical Modeling of Population Growth Using Differential Equations. Iconic Research And Engineering Journals, 10(1). doi: https://doi.org/10.64388/IREV10I1-1722138
Lavkush Pandey "Mathematical Modeling of Population Growth Using Differential Equations" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026. Crossref, https://doi.org/10.64388/IREV10I1-1722138
@article{1722138,
      author = {Lavkush Pandey},
      title = {Mathematical Modeling of Population Growth Using Differential Equations},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {3958-3963},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722138.pdf},
      abstract = {Population growth is a central topic in mathematical biology and ecological modeling. This paper analyzes population dynamics using exponential, logistic, and extended nonlinear models. The study formulates first-order differential equations to describe how populations evolve over time under different environmental constraints. Analytical solutions are derived for classical models, while advanced extensions such as harvesting effects, Allee thresholds, time-dependent growth rates, delayed feedback systems, and predator–prey interactions are also introduced. Results show that exponential models describe ideal conditions, while logistic and nonlinear models provide realistic and stable long-term population behavior.},
      keywords = {Logistic Growth, Exponential Growth, Carrying Capacity, Stability Analysis, Ecological Systems, Population Biology.},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1722138}
  }