Скачать или смотреть Logistic Growth Model | Python Simulation | Solved Example

In this video, you will learn how to model population growth using the logistic growth model, which describes how a population grows with limited resources and stabilizes at a carrying capacity.

The logistic differential equation is derived step by step, including how to separate variables, apply partial fractions, and solve for the population as a function of time. You’ll also see how the solution behaves as time goes to infinity and why the population levels off instead of growing exponentially forever.

The tutorial includes a practical section where you will implement a numerical simulation of the logistic growth equation in Python, using Euler’s method. You’ll learn how to write the code, run the simulation for given parameters, and plot the population over time to visualize how it approaches the carrying capacity.

By the end of the video, you will understand the intuition behind logistic growth, its real-world applications in ecology, epidemiology, and economics, and how to simulate similar models in Python for other systems with resource constraints.

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Chapters in the video:
0:00 Introduction
0:33 Problem Statement
01:54 Objective
03:00 Exponential Growth Model
03:50 Derive the solution
07:20 Python Simulation
10:30 Conclusion

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