Chaos vs purely exponentially growing systems

In summary, the passage confusingly says that chaotic systems tend to explore a larger variety of regions of their phase space, even if those regions are not periodic. Topological mixing is introduced as a way of making the system not be exactly periodic, and it is important for proper chaotic behavior.
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Sunny Singh
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I have just started reading chaos from the MIT OpenCourseWare and the following passage has confused me.

"The sensitivity to initial conditions is important to chaos but does not itself differentiate from simple exponential growth, so the aperiodic behavior is also important. In the definition of this somewhat undescriptive phrase we include that the system should undergo Topological Mixing. This means that any points starting in a region (open set) of the phase space will evolve to overlap any other region of the phase space, so chaotic systems tend to explore a larger variety of regions of the phase space"

IF the solution of a system described by linear equations is exponential, then it is aperiodic too right? it won't fall into a periodic orbit for sure. Then how is it not considered to be chaotic? I might be misunderstanding "Topological mixing" here. How does this topological mixing thing leads to such a system not getting called chaotic?
 
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If it is exponential, it is still predictable: while the absolute error diverges, the relative uncertainty stays the same. Start with a 1% uncertainty and you still have a 1% uncertainty later. That is not considered chaotic. To make a more formal definition, this topological mixing is introduced: While the system cannot be exactly periodic, it should come close to its original conditions again (to avoid cases like eternal exponential growth), and then you get proper chaotic behavior.
 
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If you make a 1-particle system that is like a harmonic oscillator, but has a "wrong" sign in the potential energy: ##V(x)=-\frac{1}{2}kx^2##, it has an unstable equilibrium point at ##x=0## and an arbitrarily small deviation from that equilibrium will grow exponentially, but it's not something that would be called chaotic. Systems with actual chaos always have higher than 2nd powers in the potential energy function.
 
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mfb said:
If it is exponential, it is still predictable: while the absolute error diverges, the relative uncertainty stays the same. Start with a 1% uncertainty and you still have a 1% uncertainty later. That is not considered chaotic. To make a more formal definition, this topological mixing is introduced: While the system cannot be exactly periodic, it should come close to its original conditions again (to avoid cases like eternal exponential growth), and then you get proper chaotic behavior.

So this means that given enough time, the system will necessarily return arbitrarily close to its initial conditions? Does it also mean that the system eventually explores the full volume of its phase space accessible to it with the constraints of energy? Can you please give me some link to topological mixing that isn't too hard on mathemaics? because most of the places i found about it were articles from mathematics departments and i find their notations a bit hard to follow. Thank you so much helping me out.
 

1. What is the main difference between chaos and purely exponentially growing systems?

Chaos refers to a complex, unpredictable behavior in a system that is highly sensitive to initial conditions. On the other hand, purely exponentially growing systems exhibit a simple, steady growth over time.

2. Can chaos and purely exponentially growing systems coexist in a single system?

Yes, it is possible for a system to exhibit both chaotic behavior and purely exponential growth. This can occur when there are multiple factors influencing the system and some exhibit chaotic behavior while others follow an exponential growth pattern.

3. How can we determine if a system is chaotic or purely exponentially growing?

One way to determine the nature of a system is to analyze its behavior over time. If the system displays a sensitive dependence on initial conditions and exhibits unpredictable behavior, it is likely chaotic. If the system shows a consistent, steady growth, it is likely purely exponentially growing.

4. Are there any real-world examples of chaotic systems?

Yes, there are many examples of chaotic systems in nature and in man-made systems. Some common examples include weather patterns, population dynamics, and the stock market. Chaos can also be observed in chaotic attractors, such as the Lorenz attractor.

5. Can chaos be controlled or predicted?

While chaos cannot be completely controlled or predicted, there are techniques and methods that can help understand and manage chaotic systems. These include sensitive dependence on initial conditions, bifurcation analysis, and chaos theory. However, due to the complex and unpredictable nature of chaos, it is not possible to fully control or predict it.

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