1. Announcements Topics: -Introduction to (review of) Differential Equations (Chapter 6) -Euler’s Method for Solving DEs (introduced in 6.1) -Analysis of Autonomous DEs – Population Models (7.1) Work On: - Read pages 410 – 415 in section 6.1 on Euler’s method; read section 7.1 in the textbook -Practice Problems in Section 6.1 and 7.1 (posted on webpage) -Assignments 0 and 1 (posted on webpage shortly)

2. Differential Equations A differential equation (DE) is an equation that involves an unknown function and one or more of its derivatives. Examples:

3. Differential Equations A solution of a differential equation is a function that, along with its derivatives, satisfies the DE. Example 7.1, #26. Show that is the solution of the differential equation with initial condition.

4. Differential Equations In general, a differential equation has a whole family of solutions. Example:

5. Differential Equations An initial value problem (IVP) provides an initial condition so you can find a particular solution. Example:

6. Modelling: Verbal Descriptions IVPs Example: Write a differential equation and an initial condition to describe the following events. 7.1, #10. The relative rate of change of the population of wild foxes in an ecosystem is 0.75 baby foxes per fox per month. Initially, the population is 74 thousand. 7.1, #12. The rate of change of the thickness of the ice on a lake is inversely proportional to the square root of its thickness. Initially, the ice is 3 mm thick.

7. Solutions for General DEs  Algebraic Solutions  an explicit formula or algorithm for the solution (often, impossible to find)  Geometric Solutions  a sketch of the solution obtained from analyzing the DE  Numeric Solutions  an approximation of the solution using technology and and some estimation method, such as Euler’s method

8. Algebraic Solutions Example 1: Find the general solution of the pure-time DE Example 2: Find the general solution of the pure-time DE

9. Algebraic Solutions Example 3: Find the solution of the autonomous DE with initial condition

10. Geometric Solutions Example: Sketch the graph of the solution to the DE given an initial condition of

11. Euler’s Method What information does an IVP tell us about the solution? Example: DE: IC: slope of the solution curve y(x) an exact value of the solution

12. Euler’s Method Euler’s Idea: First, using the initial condition as a base point, approximate the solution curve y(x) by its tangent line. First Euler approximation

13. Euler’s Method Next, travel a short distance along this line, determine the slope at the new location (using the DE), and then proceed in that ‘corrected’ direction. Euler’s approximation with step size h= 0.5

14. Euler’s Method Repeat, correcting your direction midcourse using the DE at regular intervals to obtain an approximate solution of the IVP. By increasing the number of midcourse corrections, we can improve our estimation of the solution. Euler approximation with step size 0.25

15. Euler’s Method Summary: An approximate solution to the IVP is generated by choosing a step size and computing values according to the algorithm

16. Euler’s Method Algorithm: Algorithm In Words: next time step = previous time step + step size next approximation = previous approximation + rate of change of the function x step size

17. Example Consider the IVP (a) Approximate the value of the solution at x=1 by applying Euler’s method and using a step size of h=0.25. (b) Compare with the exact result.

18. Example Calculations: x n = x n-1 + h y n = approx. value of solution at x n x 0 = 0y 0 = 2 Table of Approximate Values for the Solution y(x) of the IVP

19. Example Graph of Approximate Solution: Plot points and connect with straight line segments. x n = x n-1 + h y n = approx. value of solution at x n x 0 = 0y 0 = 2 x 1 = 0.25y 1 = 2.75 x 2 = 0.5y 2 = 3.5625 x 3 = 0.75y 3 = 4.4375 x 4 = 1y 4 = 5.375

20. Example Consider the IVP Approximate P(1) using Euler’s method and a step size of h=0.5. Note: We are not able to find an exact solution for this IVP.

21. Example Calculations: t n = t n-1 + hP n = approx. value of solution at t n t 0 = 0P 0 = 5 Table of Approximate Values for the Solution P(t) of the IVP

22. Example Graph of Approximate Solution: Plot points and connect with straight line segments. t n = t n-1 + hP n = approx. value of solution at t n t 0 = 0P 0 = 5

23. Qualitative Analysis of a DE We can analyze DEs qualitatively to determine important qualities or characteristics of the solutions without explicitly solving for one. Often this is all that is necessary to answer a given question or problem.

24. Qualitative Analysis of a DE Example: Consider the following autonomous DE describing the growth of a certain population. When is the population constant? When is the population increasing? When is it decreasing?

25. Modeling Start with a simple model (differential equation) to roughly explain how a system changes then modify so it fits real-life observable data as close as possible. If you then observe an initial condition, you can use this rule (DE) to generate a solution and use it to predict future values

26. Basic Exponential Model Model: where is the number of individuals at time t; is the initial population; and k =constant. Solution:

27. Basic Exponential Model Example: Suppose we know that the growth rate of a population is half of its current population and the initial population is 10. Then we have the model Analyze the dynamics of this population.

28. Basic Exponential Model Relative Rate of Change/Per Capita Production Rate: In this example, So the per capita growth rate is (on average) 0.5 people/year per person. growth rate per person is constant # individuals/unit of time/individual

29. Basic Exponential Model Solution:

30. Basic Exponential Model

31. Summary: This model describes a population that grows at a rate proportional to its size. It assumes ideal conditions, i.e. unlimited resources, no predators, no disease, etc.

32. Logistic Model Model: k = positive constant L = carrying capacity carrying capacity: the maximum population that the environment is capable of sustaining in the long run

33. Logistic Model Example: A population grows according to the logistic model with initial population Analyze the dynamics of this population.

34. Logistic Model Equilibrium Solutions:

35. Logistic Model Behaviour of Solutions P(t):

36. Logistic Model Note:

37. Logistic Model Some Solution Curves + Solution to IVP:

38. Logistic Model Summary: This model describes a population that grows exponentially for small values of P but as P increases, the growth rate slows down and the population approaches the carrying capacity. If the population starts above its carrying capacity, it will decrease towards the carrying capacity.

39. Modified Logistic Differential Equation (the Allee Effect) Model: where k, m, and L are positive constants and m< L L=carrying capacity m=existential threshold

40. Modified Logistic Differential Equation (the Allee Effect) Example: A population grows according to the modified logistic model Analyze the dynamics of this population.

41. Modified Logistic Differential Equation (the Allee Effect) Equilibrium Solutions:

42. Modified Logistic Differential Equation (the Allee Effect) Behaviour of Solutions P(t):

43. Modified Logistic Differential Equation (the Allee Effect) Some Solution Curves:

44. Modified Logistic Differential Equation (the Allee Effect) Summary: This model is similar to the logistic model but includes the idea of an existential threshold – the minimum number of individuals needed to sustain a population. If the population falls below this number, it will die out (decrease to 0).

45. Selection Model Consider two variations of a certain population that grow at a rate proportional to their size. a(t)=population size of type a at time t; =per capita production rate of type a; b(t)=population size of type b at time t; =per capita production rate of type b.

46. Selection Model It is often difficult to count the exact number of individuals for some populations, so instead we measure the fraction or proportion of each present in the total population. # of individuals of type a total population size

47. Selection Model The rate of change of the fraction of type a can be expressed as a logistic (autonomous) equation: Calculations: a measure of the strength of selection

48. Selection Model Solution: where Calculations:

49. Selection Model Example: Suppose we find two strains of bacteria, type a and type b, where the per capita production for a is 0.5 and for b is 0.3. (a)Write differential equations for the growth rate of each strain..

50. Selection Model (b) Write an autonomous DE for p, the fraction of type a bacteria present in the sample. (c) Given that initially 10% of the population is type a, write the solution for p and use it to find the fraction of type a bacteria present after 2 hours..

51. Selection Model (d) Use Euler’s Method with a step size of 1 to approximate the fraction of type a after 2 hours. (Compare to answer in (c)).

52. Selection Model (e) What happens as ? (f) Graph the solution..

53. Selection Model Summary: This model describes two variations of some population competing for the same resources. The rate of change of the fraction of type a is modeled by a logistic equation. If the per capita production rate of type a is greater than that of type b, then type a will take over (i.e. the fraction of type a present will approach 1) and vice versa.