1. Lecture 18 - Numerical Differentiation
CVEN 302
July 19, 2002

2. Lecture’s Goals Finite Difference Schemes
Taylor Series Expansion for Differentiation
Basic Numerical Integration
Trapezoidal Rule
Simpson’s Rule
Midpoint
Gaussian Quadrature

3. Calculus - Numerical Methods
Differentiation
Integration v t dv dt v t a t y

4. Numerical Differentiation
Estimate the derivatives (slope, curvature, etc.) of a function by using the function values at only a set of discrete points
Ordinary differential equation (ODE)
Partial differential equation (PDE)

5. Numerical Differentiation
Represent the function by Taylor polynomials or Lagrange interpolation
Evaluate the derivatives of the interpolation polynomial at selected nodal points

6. Numerical Differentiation
A Taylor series or Lagrange interpolation of points can be used to find the derivatives. The Taylor series expansion is defined as:

7. First Derivative at a Point
i i i i i+2

8. Numerical Differentiation
Use the Taylor series expansion to represent three points about single location:

9. Numerical Differentiation
Assume that the data points are “equally spaced” and the equations can be written as:

10. Forward Differentiation
For a forward first derivative, subtract eqn[2] from eqn[1]:
Rearrange the equation:

11. Forward Differentiation
As the Dx gets smaller the error will get smaller
The error is defined as:

12. Backward Differentiation
Subtract eqn[3] from eqn[2]:
The error is defined as:

13. Central Differentiation
Subtract eqn[3] from eqn[1]:
The error is defined as:

14. Differential Error
Notice that the errors of the forward and backward 1st derivative of the equations have an error of the order of O(Dx) and the central differentiation has an error of order O(Dx2). The central difference has an better accuracy and lower error that the others. This can be improved by using more terms to model the first derivative.

15. Numerical Differentiation
If you want to improve the accuracy and decrease the error you will need to eliminate terms : = =

16. Higher Order Errors in Differentiation
The terms become :
The terms become A=-3, B= 4 and C=-1

17. Higher Order 1st Derivative
Parabolic curve
i i i i i+2
Forward difference
Backward difference

18. Higher Order Derivatives
To find higher derivatives, use the Taylor series expansions of term and eliminate the terms from the sum of equations. To improve the error in the problem add additional terms.

19. 2nd Derivative of the Function
It will require three terms to get a central 2nd derivative of discrete set of data. = # = =

20. 2nd Order Central Difference
The terms become :
The terms become A=1,B=-2 and C=1. Therefore

21. Lagrange Differentiation
Another form of differentiation is to use the Lagrange interpolation between three points. The values can be determine for unevenly spaced points. Given:

22. Lagrange Differentiation
Differentiate the Lagrange interpolation
Assume a constant spacing

23. Lagrange Differentiation
Differentiate the Lagrange interpolation
Various locations

24. Lagrange Differentiation
To find a higher order derivative from the Lagrange interpolation for a three point Lagrange
Take the derivative

25. Partial Derivatives
Straightforward extension of one-dimensional formula
(i, j+2)
(i-1, j+1)
(i, j+1)
(i+1, j+1)
(i-2, j)
(i-1, j)
(i, j)
(i+1, j)
(i+2, j)
(i-1, j-1)
(i, j-1)
(i+1, j-1)
(i, j-2)

26. (i, j+2)
(i-1, j+1)
(i, j+1)
(i+1, j+1)
(i-2, j)
(i-1, j)
(i, j)
(i+1, j)
(i+2, j)
(i-1, j-1)
(i, j-1)
(i+1, j-1)
(i, j-2)

27. Partial Derivatives
Laplacian Operator
j+1 j
j-1
i-1 i
i+1

28. Partial Derivatives
Mixed Derivative
j+1 j
j-1
i-1 i
i+1

29. Bi-harmonic Operator
j+2
j+1 j
j-1
j-2
i-2
i-1
i+1
i+2 i

30. Richardson Extrapolation
This technique uses the concept of variable grid sizes to reduce the error. The technique uses a simple method for eliminating the error. Consider a second order central difference technique. Write the equation in the form:

31. Richardson Extrapolation
The central difference can be defined as
Write the equation with different grid sizes

32. Richardson Extrapolation
Expand the terms:

33. Richardson Extrapolation
Multiply eqn [2] by 4 and subtract eqn [1] from it.

34. Richardson Extrapolation
The equation can be rewritten as:
It can be rewritten in the form

35. Richardson Extrapolation
The technique can be extrapolated to include the higher order error elimination by using a finer grid.

36. Richardson Extrapolation Example
The function is given:
Find the first derivative at x=1.25 using a central difference scheme and Dh = The exact solution:

37. Richardson Extrapolation Example
The data points are:
The derivatives using central difference

38. Richardson Extrapolation Example
The results of the central difference scheme are:
The Richardson Extrapolation uses these results to find a better solution

39. Summary Finite Difference Techniques Error Calculation Taylor Series
Lagrange Polynomials
Error Calculation

40. Summary Finite Difference Techniques Forward Difference Scheme
Backward Difference Scheme
Central Difference Scheme

41. Homework
Check the Homework webpage