I don't get the Slowly Varying Envelope Approximation

In summary, the SVEA equation states that the envelope is almost linear everywhere with very minimal bends. This approximation is valid for regions of ##t<0## and ##t>0##, but at the turning point ##t=0## the function may bends downwards too steeply. To avoid this, the transition must be occurring with a somehow very long duration of time.
  • #1
carllacan
274
3
Hi.

I can't for the life of me understand the math behind the SVEA. I graphically/intuitively understand what it means that the envelope varies slowly, but I can't connect that with the mathematical expression: $$ \left \vert \frac{\partial ^2 E_0}{\partial t ^2} \right \vert << \left \vert \omega\frac{\partial E_0}{\partial t}\right \vert $$

If the field is sinusoidal then the first derivative should simply add a factor of ω , so that $$ \left \vert \frac{\partial ^2 E_0}{\partial t ^2} \right \vert = \left \vert \omega\frac{\partial E_0}{\partial t}\right \vert $$

but instead the approximation says that <<. What am I missing?

THank you for your time.
 
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  • #2
SVEA means that the envelope is almost linear everywhere with very minimal bends. That's how you come with
$$
\left \vert \frac{\partial ^2 E_0}{\partial t ^2} \right \vert << \left \vert \omega\frac{\partial E_0}{\partial t}\right \vert
$$
If the above inequality holds, you don't need to worry about the higher order derivatives, they are guaranteed to be much smaller than the first derivative.
You can imagine a function which increases monotonically and is very close to being linear for ##t<0##. In ##t>0##, it's also very close to linear but keeps decreasing. In these two regions, the above equality can be seen to hold with a high degree of validity. But at the turning point ##t=0##, the function may bends downwards too steeply such that the second derivative cannot be neglected at this point. To avoid this, the transition must be occurring with a somehow very long duration of time such that everywhere the function's second derivative is negligible. Just imagining this picture in mind should lead you to conclude that this function must vary slowly.
carllacan said:
If the field is sinusoidal then the first derivative should simply add a factor of ω
##E_0## is the envelope, the sinusoidal oscillation is already excluded.
 
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  • #3
blue_leaf77 said:
SVEA means that the envelope is almost linear everywhere with very minimal bends. That's how you come with
$$
\left \vert \frac{\partial ^2 E_0}{\partial t ^2} \right \vert << \left \vert \omega\frac{\partial E_0}{\partial t}\right \vert
$$
If the above inequality holds, you don't need to worry about the higher order derivatives, they are guaranteed to be much smaller than the first derivative.
You can imagine a function which increases monotonically and is very close to being linear for ##t<0##. In ##t>0##, it's also very close to linear but keeps decreasing. In these two regions, the above equality can be seen to hold with a high degree of validity. But at the turning point ##t=0##, the function may bends downwards too steeply such that the second derivative cannot be neglected at this point. To avoid this, the transition must be occurring with a somehow very long duration of time such that everywhere the function's second derivative is negligible. Just imagining this picture in mind should lead you to conclude that this function must vary slowly.

##E-0## is the envelope, the sinusoidal oscillation is already excluded.
All clear now, thanks!
 

What is the Slowly Varying Envelope Approximation (SVEA)?

The Slowly Varying Envelope Approximation (SVEA) is a mathematical technique used in physics and engineering to simplify the analysis of systems with rapidly varying components. It involves separating the rapidly varying part of a system from the slowly varying part, and then assuming that the slowly varying part does not significantly affect the behavior of the rapidly varying part.

Why is the SVEA important in scientific research?

The SVEA is important because it allows scientists to simplify complex systems and make them more manageable to analyze. This can save time and computational resources, and also provide insights into the behavior of the system that may not be apparent when considering the system as a whole.

How is the SVEA different from other approximation techniques?

The SVEA is different from other approximation techniques in that it specifically focuses on separating the rapidly varying and slowly varying components of a system. Other techniques, such as the perturbation method, may also be used to simplify complex systems, but they may not take into account the effects of rapidly varying components.

What are the limitations of the SVEA?

One limitation of the SVEA is that it assumes the slowly varying part of a system does not significantly affect the behavior of the rapidly varying part. This may not always be the case, and in some situations, the slow and rapid components may interact in a way that cannot be accurately described by the SVEA.

Additionally, the SVEA may not be applicable to systems with non-linear behavior.

How is the SVEA used in specific fields of science?

The SVEA is used in a variety of fields, such as optics, acoustics, and fluid dynamics, to simplify the analysis of complex systems. For example, in optics, the SVEA is used to study the propagation of light in non-uniform media, and in fluid dynamics, it is used to analyze the behavior of waves in non-uniform flow.

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