# Modeling with Differential Equations Contributed by: In this section, we will learn: How to represent some mathematical models in the form of differential equations.
1. 9
DIFFERENTIAL EQUATIONS
2. DIFFERENTIAL EQUATIONS
Perhaps the most important of
all the applications of calculus is
to differential equations.
3. DIFFERENTIAL EQUATIONS
When physical or social scientists use
calculus, more often than not, it is to analyze
a differential equation that has arisen in
the process of modeling some phenomenon
they are studying.
4. DIFFERENTIAL EQUATIONS
It is often impossible to find an explicit
formula for the solution of a differential
 Nevertheless, we will see that graphical and numerical
approaches provide the needed information.
5. DIFFERENTIAL EQUATIONS
9.1
Modeling with
Differential Equations
In this section, we will learn:
How to represent some mathematical models
in the form of differential equations.
6. MODELING WITH DIFFERENTIAL EQUATIONS
In describing the process of modeling in
Section 1.2, we talked about formulating
a mathematical model of a real-world problem
through either:
 Intuitive reasoning about the phenomenon
 A physical law based on evidence from experiments
7. DIFFERENTIAL EQUATION
The model often takes the form of
a differential equation.
 This is an equation that contains an unknown
function and some of its derivatives.
8. MODELING WITH DIFFERENTIAL EQUATIONS
This is not surprising.
 In a real-world problem, we often notice that
changes occur, and we want to predict future behavior
on the basis of how current values change.
9. MODELING WITH DIFFERENTIAL EQUATIONS
Let’s begin by examining several
examples of how differential equations
arise when we model physical
10. MODELS OF POPULATION GROWTH
One model for the growth of a population
is based on the assumption that the
population grows at a rate proportional to the
size of the population.
11. MODELS OF POPULATION GROWTH
That is a reasonable assumption for
a population of bacteria or animals under
ideal conditions, such as:
 Unlimited environment
 Absence of predators
 Immunity from disease
12. MODELS OF POPULATION GROWTH
Let’s identify and name the variables
in this model:
 t = time (independent variable)
 P = the number of individuals in the population
(dependent variable)
13. MODELS OF POPULATION GROWTH
The rate of growth of
the population is the derivative
14. POPULATION GROWTH MODELS Equation 1
Hence, our assumption that the rate of
growth of the population is proportional to
the population size is written as the equation
dP
kP
dt
where k is the proportionality constant.
15. POPULATION GROWTH MODELS
Equation 1 is our first model for
population growth.
 It is a differential equation because it contains
an unknown function P and its derivative dP/dt.
16. POPULATION GROWTH MODELS
Having formulated
a model, let’s look at its
17. POPULATION GROWTH MODELS
If we rule out a population of 0, then
P(t) > 0 for all t
So, if k > 0, then Equation 1 shows that:
P’(t) > 0 for all t
18. POPULATION GROWTH MODELS
This means that the population is
always increasing.
 In fact, as P(t) increases, Equation 1 shows that
dP/dt becomes larger.
 In other words, the growth rate increases as
the population increases.
19. POPULATION GROWTH MODELS
Equation 1 asks us to find a function whose
derivative is a constant multiple of itself.
 We know from Chapter 3 that exponential functions
have that property.
 In fact, if we let P(t) = Cekt, then
P’(t) = C(kekt) = k(Cekt) = kP(t)
20. POPULATION GROWTH MODELS
Thus, any exponential function
of the form P(t) = Cekt
is a solution of Equation 1.
 In Section 9.4, we will see that there is
no other solution.
21. POPULATION GROWTH MODELS
Allowing C to vary through all the real
numbers, we get the family of solutions
P(t) = Cekt, whose graphs are shown.
22. POPULATION GROWTH MODELS
However, populations have only
positive values.
 So, we are interested only in the solutions with C > 0.
 Also, we are probably concerned only with values of t
greater than the initial time t = 0.
23. POPULATION GROWTH MODELS
The figure shows the physically
meaningful solutions.
24. POPULATION GROWTH MODELS
Putting t = 0, we get:
P(0) = Cek(0) = C
 The constant C turns out to be
the initial population, P(0).
25. POPULATION GROWTH MODELS
Equation 1 is appropriate for modeling
population growth under ideal conditions.
However, we have to recognize that a more
realistic model must reflect the fact that
a given environment has limited resources.
26. POPULATION GROWTH MODELS
Many populations start by increasing in
an exponential manner.
However, the population levels off when
it approaches its carrying capacity K
(or decreases toward K if it ever exceeds K.)
27. POPULATION GROWTH MODELS
For a model to take into account both
trends, we make two assumptions:
dP
kP
1. dt if P is small.
(Initially, the growth rate is proportional to P.)
dP
0
2. dt if P > K.
(P decreases if it ever exceeds K.)
28. POPULATION GROWTH MODELS Equation 2
A simple expression that incorporates both
assumptions is given by the equation
dP  P
kP  1  
dt  K
 If P is small compared with K, then P/K is close to 0.
So, dP/dt ≈ kP
 If P > K, then 1 – P/K is negative. So, dP/dt < 0
29. LOGISTIC DIFFERENTIAL EQUATION
Equation 2 is called the logistic
differential equation.
 It was proposed by the Dutch mathematical biologist
Pierre-François Verhulst in the 1840s—as a model
for world population growth.
30. LOGISTIC DIFFERENTIAL EQUATIONS
In Section 9.4, we will develop techniques
that enable us to find explicit solutions of
the logistic equation.
 For now, we can deduce qualitative characteristics
of the solutions directly from Equation 2.
31. POPULATION GROWTH MODELS
We first observe that the constant
functions P(t) = 0 and P(t) = K are
 This is because, in either case, one of the factors
on the right side of Equation 2 is zero.
32. EQUILIBRIUM SOLUTIONS
This certainly makes physical sense.
If the population is ever either 0 or at
the carrying capacity, it stays that way.
 These two constant solutions are called
equilibrium solutions.
33. POPULATION GROWTH MODELS
If the initial population P(0) lies between
0 and K, then the right side of Equation 2
is positive.
 So, dP/dt > 0 and the population increases.
34. POPULATION GROWTH MODELS
However, if the population exceeds
the carrying capacity (P > K), then 1 – P/K
is negative.
 So, dP/dt < 0 and the population decreases.
35. POPULATION GROWTH MODELS
Notice that, in either case, if the population
approaches the carrying capacity (P → K),
then dP/dt → 0.
 This means the population levels off.
36. POPULATION GROWTH MODELS
So, we expect that the solutions of the logistic
differential equation have graphs that look
something like these.
37. POPULATION GROWTH MODELS
Notice that the graphs move away from
the equilibrium solution P = 0 and move
toward the equilibrium solution P = K.
38. MODELING WITH DIFFERENTIAL EQUATIONS
Let’s now look at an example
of a model from the physical
39. MODEL FOR MOTION OF A SPRING
We consider the motion of an object
with mass m at the end of a vertical
40. MODEL FOR MOTION OF A SPRING
In Section 6.4, we discussed
Hooke’s Law.
 If the spring is stretched (or compressed) x units from
its natural length, it exerts a force proportional to x:
restoring force = -kx
where k is a positive constant (the spring constant).
41. SPRING MOTION MODEL Equation 3
If we ignore any external resisting forces
(due to air resistance or friction) then,
by Newton’s Second Law, we have:
2
d x
m 2  kx
dt
42. SECOND-ORDER DIFFERENTIAL EQUATION
This is an example of a second-order
differential equation.
 It involves second derivatives.
43. SPRING MOTION MODEL
Let’s see what we can guess about
the form of the solution directly from
the equation.
44. SPRING MOTION MODEL
We can rewrite Equation 3 in the form
2
d x k
2
 x
dt m
 This says that the second derivative of x
is proportional to x but has the opposite sign.
45. SPRING MOTION MODEL
We know two functions with this property,
the sine and cosine functions.
 It turns out that all solutions of Equation 3
can be written as combinations of certain
sine and cosine functions.
46. SPRING MOTION MODEL
This is not surprising.
 We expect the spring to oscillate about
its equilibrium position.
 So, it is natural to think that trigonometric
functions are involved.
47. GENERAL DIFFERENTIAL EQUATIONS
In general, a differential equation is
an equation that contains an unknown
function and one or more of its derivatives.
48. The order of a differential equation is
the order of the highest derivative that
occurs in the equation.
 Equations 1 and 2 are first-order equations.
 Equation 3 is a second-order equation.
49. INDEPENDENT VARIABLE
In all three equations, the independent
variable is called t and represents time.
However, in general, it doesn’t have to
represent time.
50. INDEPENDENT VARIABLE Equation 4
For example, when we consider
the differential equation
y’ = xy
it is understood that y is an unknown
function of x.
51. A function f is called a solution of a differential
equation if the equation is satisfied when
y = f(x) and its derivatives are substituted
into the equation.
 Thus, f is a solution of Equation 4 if
f’(x) = xf(x)
for all values of x in some interval.
52. SOLVING DIFFERENTIAL EQUATIONS
When we are asked to solve a differential
equation, we are expected to find all possible
solutions of the equation.
 We have already solved some particularly simple
differential equations—namely, those of the form
y’ = f(x)
53. SOLVING DIFFERENTIAL EQUATIONS
For instance, we know that the general
solution of the differential equation y’ = x3
4
is given by x
y  C
4
where C is an arbitrary constant.
54. SOLVING DIFFERENTIAL EQUATIONS
However, in general, solving
a differential equation is not an easy
 There is no systematic technique that enables
us to solve all differential equations.
55. SOLVING DIFFERENTIAL EQUATIONS
In Section 9.2, though, we will see how to
draw rough graphs of solutions even when
we have no explicit formula.
We will also learn how to find numerical
approximations to solutions.
56. SOLVING DIFFERENTIAL EQNS. Example 1
Show that every member of the family
of functions 1  ce t
y t
1  ce
is a solution of the differential equation
1
y '  2  y  1
2
57. SOLVING DIFFERENTIAL EQNS. Example 1
We use the Quotient Rule to differentiate
the expression for y:
y' 
 1  ce t
  
ce t
 1  ce t
 
 ce t
t 2
 1  ce 
cet  c 2e 2t  cet  c 2 e 2t 2cet
 
t 2 t 2
 1  ce   1  ce 
58. SOLVING DIFFERENTIAL EQNS. Example 1
The right side of the differential equation
 t 2 
1 1  1  ce 
2
y 2
 1    t 
 1
2   1  ce  

  1  cet  2   1  cet  2 
1 
 2
2  1  ce t
 
 
t t
1 4ce 2ce
 2
 2
2  1  ce 
t
 1  ce 
t
59. SOLVING DIFFERENTIAL EQNS. Example 1
Therefore, for every value of c,
the given function is a solution of
the differential equation.
60. SOLVING DIFFERENTIAL EQNS.
The figure shows graphs of seven members
of the family in Example 1.
 The differential equation shows that,
if y ≈ ±1, then y’ ≈ 0.
 This is borne out
by the flatness of
the graphs near
y = 1 and y = -1.
61. SOLVING DIFFERENTIAL EQNS.
When applying differential equations, we are
usually not as interested in finding a family
of solutions (the general solution) as we are
in finding a solution that satisfies some
 In many physical problems, we need to find
the particular solution that satisfies a condition
of the form y(t0) = y0
62. INITIAL CONDITION & INITIAL-VALUE PROBLEM
This is called an initial condition.
The problem of finding a solution of
the differential equation that satisfies
the initial condition is called an initial-value
63. INITIAL CONDITION
Geometrically, when we impose an initial
condition, we look at the family of solution
curves and pick the one that passes through
the point (t0, y0).
64. INITIAL CONDITION
Physically, this corresponds to measuring
the state of a system at time t0 and using
the solution of the initial-value problem
to predict the future behavior of the system.
65. INITIAL CONDITION Example 2
Find a solution of the differential equation
1
y' 2 y 2
 1
that satisfies the initial condition y(0) = 2.
66. INITIAL CONDITION Example 2
Substituting the values t = 0 and y = 2
into the formula from Example 1,
t
1  ce
y
1  cet
0
we get: 1  ce 1  c
2 0

1  ce 1  c
67. INITIAL CONDITION Example 2
Solving this equation for c,
we get:
2 – 2c = 1 + c
This gives c = ⅓.
68. INITIAL CONDITION Example 2
So, the solution of the initial-value
problem is:
1 t
1 e 3  e
3
t
y t

1 t
1 e 3  e
3