Solving differential equations is a fundamental skill in mathematics, science, and engineering. These equations describe how quantities change over time, making them essential for modeling real-world systems — from population growth and disease spread to physics simulations and financial predictions.
Understanding how to solve them analytically (as in this example) not only deepens mathematical intuition but also complements programming-based approaches. In many cases, numerical solvers in code (e.g., using Python or MATLAB) are used to approximate solutions, but knowing the underlying math helps debug, optimize, and better interpret those results.
This note walks through the full solution of a logistic growth model — a classic real-world example of a differential equation with limits on growth.
2The Equation
We are given the following logistic differential equation:
dtdN=9⋅10−6⋅N(7000−N)
Initial condition:
N(0)=600
3Step 1: Separation of Variables
Separate variables:
N(7000−N)1dN=9⋅10−6dt
4Step 2: Partial Fraction Decomposition
We split the left-hand side:
N(7000−N)1=NA+7000−NB
Multiply through:
1=A(7000−N)+BN
Solve for A and B:
Let N=0⇒A=70001
Let N=7000⇒B=70001
So:
N(7000−N)1=70001(N1+7000−N1)
5Step 3: Integration
70001∫(N1+7000−N1)dN=∫9⋅10−6dt
Integrate:
70001(ln∣N∣−ln∣7000−N∣)=9⋅10−6t+C
ln∣∣7000−NN∣∣=0.063t+C
6Step 4: Solve for N(t)
Exponentiate both sides:
7000−NN=e0.063t+C=Ke0.063t
Solve for N:
N=1+Ke0.063t7000⋅Ke0.063t
7Step 5: Apply Initial Condition
Given: N(0)=600
600=1+K7000K⇒600+600K=7000K⇒600=6400K⇒K=323
8Final Solution
N(t)=1+323e0.063t7000⋅323e0.063t
9Step 6: Approximate Exponential (Optional)
The approximation e0.063t≈1.065t helps with calculator-free computation.