Question about an "exact" distribution function

In summary, the conversation discusses the derivation of the equation of motion for a system of particles in a phase-space, defined as a sum of delta-functions. It is demonstrated that in the absence of particle creation or destruction, this equation of motion can be derived from phase-space conservation. However, it is noted that this derivation does not involve any statistical averaging.
  • #1
dRic2
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Suppose I have an exact microscopic distribution function in phase-space defined as a sum of delta-functions, i.e
$$F( \mathbf x, \mathbf v, t) = \sum_{i} \delta( \mathbf x - \mathbf x_i ) \delta (\mathbf v - \mathbf v_i )$$
Can I conclude that, in absence of creation/destruction of particles,
$$ \frac {dF( \mathbf x, \mathbf v, t)}{dt} = \frac {\partial F} {\partial t} + \mathbf v \cdot \frac {\partial F} {\partial \mathbf x} + \mathbf a \cdot \frac {\partial F} {\partial \mathbf v} = 0$$
where ## \mathbf a = \frac {d \mathbf v}{dt}## Note that here no statistical averaging is involved.

I found that this is "trivially" derived from phase-space conservation, but although I get a feeling for it, I don't see it so clearly.

Thanks
Ric
 
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  • #2
Maybe something like this?

Consider a volume ##\tau## in the case space. Then ##\frac {dN \tau}{dy} = 0## because the total number of particles can not change. Then differentiation of the product yields
##\frac {dN}{dt}\tau = -N \frac {d\tau}{dt} = 0## because Liouville's theorem tells that phase space behaves as an incompressible fluid, so the volume element must be unchanged.
 
  • #3
In the OP the most important thing is missing in the intial equation, namely the (ensemble) average over the phase-space trajectories ##(x_j(t),v_j(t))## (though I'd prefer ##p_j## rather then ##v_j##).
 
  • #4
My question is what happens if I do *not* perform an ensemble average.

This is not statistical mechanics just yet, it's just a question about the *exact* equation of motion for a system of particles
 
  • #5
Then I don't understand what you are aiming at. Without an averaging the left-hand-side of the equation is not a phase-space distribution function.
 

Related to Question about an "exact" distribution function

1. What is an "exact" distribution function?

An "exact" distribution function is a mathematical function that describes the probability of a specific outcome in a given set of data. It is considered "exact" because it provides a precise probability for each possible outcome, rather than an estimate or approximation.

2. How is an "exact" distribution function different from other types of distribution functions?

An "exact" distribution function is different from other types of distribution functions, such as normal or binomial distributions, because it provides a precise probability for each possible outcome, rather than a range of probabilities or a probability density function.

3. What types of data can be described by an "exact" distribution function?

An "exact" distribution function can be used to describe any type of data that can be measured or observed, such as numerical data, categorical data, or continuous data. It is commonly used in statistics and probability to analyze and make predictions about data.

4. How is an "exact" distribution function calculated?

An "exact" distribution function is calculated using mathematical formulas and equations, based on the specific characteristics and parameters of the data being analyzed. These calculations can be done manually or with the help of statistical software.

5. What is the importance of understanding "exact" distribution functions?

Understanding "exact" distribution functions is important because it allows scientists and researchers to accurately analyze and interpret data, make predictions and inferences, and make informed decisions based on the probabilities of different outcomes. It is also a fundamental concept in statistics and probability theory.

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