Relationship between autonomous system and related single equation

In summary, the relationship between autonomous systems and related single equations is characterized by the fact that autonomous systems can be represented mathematically by ordinary differential equations (ODEs) where the system's behavior is independent of time. These equations often take the form \( \frac{dx}{dt} = f(x) \), where \( x \) represents the state variables and \( f(x) \) is a function defining the system's dynamics. The stability, equilibrium points, and trajectories of the system can be analyzed through this equation, providing insights into the system's long-term behavior and characteristics.
  • #1
zenterix
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Homework Statement
My question is about some notes from MIT OCW's 18.03 "Differential Equations" on first-order ODE autonomous systems.
Relevant Equations
See below.
Here are the notes.

We have the system

$$\begin{bmatrix} x'\\y' \end{bmatrix}=\begin{bmatrix}f(x,y)\\g(x,y)\end{bmatrix}\tag{1}$$

We eliminate ##t## by dividing one equation by the other

$$\frac{y'}{x'}=\frac{dy/dt}{dx/dt}=\frac{dy}{dx}=\frac{g(x,y)}{f(x,y)}\tag{2}$$

$$\frac{dy}{dx}=\frac{g(x,y)}{f(x,y)}\tag{3}$$

which is a single first order equation involving ##y## as a function of ##x##.

Indeed, in the older literature, little distinction was made between the system and the single equation - "solving" meant to solve either one.

There is however a difference between them: the system involves time, whereas the single ODE does not. Consider how their respective solutions are related:

$$\begin{matrix} x=x(t)\\y=y(t)\end{matrix}\implies F(x,y)=0\tag{4}$$

where the equation on the right is the result of eliminating ##t## from the pair of equations on the left.

My first question is about (4).

I am not seeing where ##F(x,y)=0## comes from.
 
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  • #2
If ##x## and ##y## are functions of ##t## and we eliminate the ##t## to get an equation with only ##x## and ##y## in it and then move all terms to the left side then we do get ##F(x,y)=0##. Is this all there is to it?
 
  • #3
The notes go on as follows
Geometrically, ##F(x,y)=0## is the equation for the trajectory of the solution ##\vec{x}(t)##. The trajectory in other words is the path traced out by the moving point ##(x(t),y(t))##; it doesn't contain any record of how fast the point was moving; it is only the track (or trace, as one sometimes says) of its motion.
This is all fine.
In the same way, we have the difference between the velocity field, which represents the left side of (4) and the direction field which represents the right side.

The velocity vectors have magnitude and sense, whereas the line segments that make up the direction field only have slope.
The velocity field is represented by the initial system of equations.

What is the direction field exactly here?

For a single equation ##y'=f(x,y)##, a direction field (aka, slope field) is just a depiction of the slope at various points in the ##xy##-plane.

In our system of equations, the equivalent of a slope field seems at first glance to be the same thing as the velocity field (but perhaps without denoting the direction of the velocity, only its slope). However, I am unsure about this.

At this point, I read the last paragraph of the notes, and it seems to agree for the most part with the paragraph above. Here it is

The passing from the left side of (4) to the right side is represented geometrically by changing each of the velocity vectors to a line segment of standard length. Even the arrowhead is dropped, since it represents the direction of increasing time, and time has been eliminated; only the slope of the vector is retained.
 
  • #4
I think the simplest thing to consider is just like, consider the curve sketched out by##(t,t^2)## compared to ##(t^3,t^6)##.

Both of them traverse the parabola ##y-x^2=0## but the behavior of a particle traveling according to each of the two functions of t is pretty different. For example what is the speed at ##x=y=1## is different for the two of them. And the slope of the parabola tells you what direction they are traveling in, but nothing about how fast they are going.
 
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FAQ: Relationship between autonomous system and related single equation

What is an autonomous system in the context of differential equations?

An autonomous system refers to a set of differential equations where the independent variable does not explicitly appear in the equations. This means that the system's behavior depends solely on the state variables and not on time or any other external factors. Autonomous systems can often be analyzed using phase portraits and stability analysis.

How does an autonomous system relate to a single differential equation?

An autonomous system can often be reduced to a single differential equation by expressing one variable in terms of the others. This single equation captures the dynamics of the entire system, allowing for simpler analysis. For example, in a two-variable autonomous system, one variable can often be expressed as a function of the other, leading to a single first-order differential equation.

What are the implications of the existence of equilibrium points in autonomous systems?

Equilibrium points in autonomous systems are critical as they represent states where the system does not change over time. The stability of these points can be analyzed using linearization techniques or phase plane analysis. If an equilibrium point is stable, small perturbations will decay back to the equilibrium, while unstable points will lead to divergence from that state.

How can one determine the stability of an autonomous system?

The stability of an autonomous system can be determined through various methods, including linearization around equilibrium points, Lyapunov functions, or by analyzing the eigenvalues of the Jacobian matrix at those points. If the real parts of all eigenvalues are negative, the equilibrium is stable; if any are positive, it is unstable.

What is the significance of the phase plane in analyzing autonomous systems?

The phase plane is a graphical representation of the trajectories of an autonomous system in the state space. It allows for a visual analysis of the system's behavior, including equilibrium points, stability, and the nature of trajectories. By studying the phase plane, one can gain insights into the long-term behavior of the system and the effects of initial conditions.

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