Nonlinear system phase portraits

In summary, the goal of this problem is to find the eigenvectors and eigenvalues of the linear system around each critical point, in order to determine the stability of the system and sketch its phase portrait. The "let X=x, Y=y" notation is used to linearize the system and simplify the calculations. The phase portraits around hyperbolic equilibrium points will be the same as the linear system in their vicinity.
  • #1
schmiggy
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Homework Statement


See attached image


Homework Equations


Classification of critical points chart (unless you remember it)


The Attempt at a Solution


See attached.

Now, I'm not entirely sure what exactly I'm doing. With linear systems, the goal is to find the eigenvectors, eigenvalues and thus the general solution. Using the general solution you can then derive the attributes of the phase portrait for that particular system to aid in sketching (such as behaviour as t -> +- infinity). Is that what I'm supposed to be doing here?

So basically, taking the critical points and substituting them into the matrix and then finding the eigenvalues and vectors for the respective matrix? Then do the same again for the next critical point? Is the goal to end up with as many general solutions as there are critical points and then combine them into one sketch?

I'm not really sure what the "let X=x, Y=y" is for either, I was trying to follow an example in my lecture notes on how to linearise the system but that's where it stops - thus why I thought the goal might be to linearise the system and then find the respective general system (for each critical point).

Also, I feel like I've done something wrong making the matrix and made up some pretend maths to get my lambda for the (0,0) critical point. What a mess! Any direction would be appreciated, thanks :)
 

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  • #2
You have a first order nonlinear system, which can be written as X' = f(X), where X is a vector.
In such systems, the phase portrait around a hyperbolic equilibrium point (an equilibrium point X* such that the real part of every eigenvalue of Df(X*) is nonzero) is the same as the phase portrait of the linear system X' = Df(X*).X, in a neighbourhood of X*.

This means that in a vicinity of X* the phase portraits are the same, and you can deduce the stability by analysing the linear system.

Remark: Df(X*) is the jacobian matrix evaluated at the equilibrium point X*.
 
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What is a nonlinear system portrait?

A nonlinear system portrait is a visual representation of the behavior of a nonlinear system. It consists of a phase portrait, which shows the relationship between the system's variables, and a time series plot, which shows how the system changes over time.

How is a nonlinear system portrait different from a linear system portrait?

A nonlinear system portrait is different from a linear system portrait in that it takes into account the nonlinear relationships between the variables of the system. This allows for a more accurate representation of the system's behavior, as linear system portraits only consider linear relationships.

What are the benefits of using a nonlinear system portrait?

Nonlinear system portraits provide a more comprehensive understanding of the behavior of a system. They can reveal patterns and relationships that may not be apparent in linear system portraits. This can be useful in various fields such as physics, biology, and economics.

How are nonlinear system portraits created?

Nonlinear system portraits are created by using mathematical models and computer simulations. The equations that describe the system are solved numerically, and the resulting data is plotted to create the phase portrait and time series plot.

What are some real-world applications of nonlinear system portraits?

Nonlinear system portraits have various real-world applications, such as in predicting weather patterns, understanding population dynamics in ecology, and analyzing economic systems. They are also used in engineering to study and design complex systems, such as in control systems and robotics.

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