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Material Type: Notes; Class: Mathematics in the Earth Sciences; Subject: Earth Sciences; University: University of California-Santa Cruz; Term: Unknown 1989;
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ES 111 Mathematical Methods in the Earth Sciences Lecture Outline 16 - Weds 19th Nov ’ Higher Order Differential Equations
Here we are going to move on the higher-order differential equations (meaning derivatives higher than first order may appear). This is a big field, so we will focus mainly on solving specific, relatively simple higher order ODE’s which often crop up in Earth Sciences. When are they useful? Especially in the equations of elasticity (e.g. seismic waves) and flexure. The general form of a n-th order linear ODE is an(x)y(n)^ + an− 1 (x)y(n−1)^ + · · · + a 1 (x)y′^ + a 0 (x)y = f (x)
where the an(x) and f (x) are functions of x. Written in this form f (x) is known as the input or driving term (e.g. in elasticity, it represents the load being applied). A linear ODE with f (x)= is called homogeneous. Thus, a homogeneous linear ODE (HLDE for short) has no driving term. Here we will focus on HLDE’s.
In general, there is more than one solution to a HLDE. Suppose that y 1 (x) and y 2 (x) are both solutions of a HLDE, then any linear combination of them
c 1 y 1 (x) + c 2 y 2 (x)
is also a solution. This is true only of HLDE’s - if the driving term is not zero, then this ability to superpose solutions will not in general be true. The general solution to an n-th order HLDE is constructed by a linear combination of n linearly independent solutions to the HLDE. This is similar to the way we can construct any vector from a linear combination of the perpendicular vectors ˆi,ˆj and ˆk. A bunch of functions are linearly independent if and only if the only solution to c 1 y 1 (x) + c 2 y 2 (x) + · · · + cnyn(x) = 0
is c 1 = c 2 = · · · = cn = 0 for some x in the range of interest. In other words, none of the functions can be constructed from a linear combination of the other functions.
A good way of thinking about this is to imagine graphs of the different functions and to see whether two graphs can be made to overlay each other at every point - if they can, then the functions are not linearly independent. Example if y 1 = x and y 2 = x^2 , are the two functions linearly independent? What about y 1 = x and y 2 = 3x? What about y 1 = sin x and y 2 = cos x? If a bunch of simultaneous equations have a unique solution, then they must all be linearly independent, in which case their matrix determinant will be non-zero (see lecture 12). Finding out whether a bunch of functions are linearly independent can be done by constructing a matrix determinant called the Wronskian, which we will not deal with here.
The particular solution of a HLDE is determined by specifying the free parameters (the c’s) in the general solution. For an n-th order HLDE, n initial conditions need to be specified to constrain the c’s.
There are two methods of solving HLDE’s we’re going to address. The first is called Reduction of Order and is a bit of a cheat, since it assumes that you already know one solution to the equation, and is most useful with second-order HLDE’s. The advantage of the approach is that you end up solving a first-order ODE, which is much easier. Let’s assume that the first (known) solution is y 1. How do we obtain this solution? Often, the best way is simply to guess! You can always substitute it in to see if it works. Given this solution, let the second (unknown) solution y 2 = v(x)y 1. By substituting this into the original HLDE and then substituting for w(x) = v′(x) you end up with a first-order ODE for w(x) which you can solve (using the integrating factor technique, or otherwise). Once this is solved, you obtain v(x) by integration and get the second solution because y 2 = v(x)y 1. An example: find both solutions to y′′^ − 2 y′^ + y = 0 Another example: find the second solution to y′′^ − y = 0
given that the first solution is ex.