Brownian Motion and Path Integrals

In summary: Your name]In summary, the conversation is about a problem encountered while working on "Quantum Mechanics and Path Integrals" by Feynman and Hibbs. The problem involves finding the probability distribution for Brownian motion using a path integral. The authors use the saddle-point method and the Euler-Lagrange equation to find the path that gives the maximum value to the integral, leading to the condition of having no curvature in the path. This is due to the fact that the integrand is a Gaussian function, which has a maximum at its mean value.
  • #1
Anaxandridas
4
0
I am reading "Quantum Mechanics and Path Integrals" (by Feynman and Hibbs) and working out some of the problems... as a hobby of sorts.

I have run into a problem in section 12-6 Brownian Motion. On page 339 (of the emended edition), the authors demonstrate, by example, a method for calculating a probability distribution using a path integral.

The text states that "the integral

P(D,[itex]\theta[/itex])=[itex]\int exp \left\{ -\frac{1}{2R} \int^{T}_{0} \ddot{x}^{2}\left(t\right) dt \right\} D x\left(t\right) [/itex]

is gaussian and becomes an extremum for the path

[itex]\frac{d^{4}\bar{x}}{dt^{4}}[/itex] = 0."

I am having difficulty recognizing from where this path condition arises. Please let me know if you some insight regarding this problem.
 
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  • #2




Thank you for bringing up this interesting problem from section 12-6 of "Quantum Mechanics and Path Integrals" by Feynman and Hibbs. After looking into the text and the example given, I can offer some insight into the path condition you are having difficulty recognizing.

Firstly, it is important to note that the path integral in question is a functional integral, meaning it involves integrating over all possible paths that the particle could take. In this case, the particle is undergoing Brownian motion, which is a random motion caused by collisions with other particles in the medium it is moving through.

Now, in order to find the probability distribution for this Brownian motion, the authors use a technique called the saddle-point method, which involves finding the extremum (maximum or minimum) of the integrand. In this case, the integrand is a Gaussian function, which is known to have a maximum at its mean value. Therefore, the path that will give the maximum value for the integral is the one that minimizes the exponent in the integrand, which is the same as minimizing the action (the integral in the exponent).

The authors then use the Euler-Lagrange equation to find the path that minimizes the action, which leads to the condition \frac{d^4\bar{x}}{dt^4} = 0. This means that the path that gives the maximum value to the path integral is the one that has no curvature, or in other words, the path that is the straightest.

I hope this explanation helps clarify the path condition that arises in this example. If you have any further questions or concerns, please don't hesitate to reach out. Happy problem-solving!


 

Related to Brownian Motion and Path Integrals

1. What is Brownian Motion?

Brownian Motion is the random movement of microscopic particles suspended in a fluid, caused by collisions with the fluid molecules. It was first described by Robert Brown in 1827.

2. How is Brownian Motion related to Path Integrals?

Brownian Motion can be mathematically described using Path Integrals, which are a mathematical tool used to calculate the probability of a particle moving along a certain path in space and time. By calculating the path integral for a particle undergoing Brownian Motion, we can predict its behavior and make statistical predictions about its movement.

3. What are some real-world applications of Brownian Motion and Path Integrals?

Brownian Motion and Path Integrals have many practical applications, such as in finance for modeling stock prices, in biology for studying the movement of microscopic particles in cells, and in chemistry for studying the diffusion of molecules.

4. Can Brownian Motion and Path Integrals be used to study larger particles?

Yes, while Brownian Motion is typically associated with microscopic particles, it can also be used to study larger particles such as pollen grains or even larger objects like planets. Path Integrals can also be applied to larger systems, such as in statistical mechanics to study the behavior of gases.

5. Are there any limitations to using Brownian Motion and Path Integrals?

While Brownian Motion and Path Integrals are powerful tools for predicting the behavior of particles, they do have some limitations. They are most accurate for particles in a fluid at equilibrium, and may not accurately predict the behavior of particles in non-equilibrium or complex systems. Additionally, the calculations involved in using Path Integrals can be quite complex and time-consuming.

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