Obtaining the Feynman path integral through the brownian motion description
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Anzahl der Teile | 43 | |
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Lizenz | CC-Namensnennung 3.0 Deutschland: Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen. | |
Identifikatoren | 10.5446/49804 (DOI) | |
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Produktionsjahr | 2020 | |
Produktionsort | Lavras |
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Transkript: Englisch(automatisch erzeugt)
00:02
Hi, my name is Lucas. I am a graduate student in physics at the Federal University of Lourdes. Today, I'm going to present a way to obtain the Feynman path integral through the Brownian motion description. To do it, there are some steps we have to pass through. First, we need to understand what the Brownian motion is, obtain the position distribution probability to the Brownian particles and
00:27
use it to construct the path integral. Then, treating the Brownian motion in an absorptive medium, we include the Feynman-Kac formula to, by modifying it, establish a map to the Feynman path integral.
00:45
The Brownian motion was first observed by Hobbes Brown, a botanic, who observed the incessant and the leiothorium movement of pollen in the water's surface. This motion characterizes a stochastic process with random positions varied in time.
01:01
Here, in this image, we can see an example of paths of Brownian particles. The Brownian motion has a large applicability. Its methods can be used to different stochastic systems, such as financial markets, condensed matter and the evolutionary theory. To describe the Brownian motion,
01:21
we need to obtain the position probability distribution. A way to do it is to use the diffusion equation as presented by equation 1. To solve this equation, we consider the initial condition that the particles begin the movement at the zero position, and using Fourier transforms, we find a Gaussian distribution as a result, with the diffusion constant as the standard deviation.
01:45
Now, we want to obtain the probability of occurrence of a path. Here, we can see two examples of Brownian motion paths that we have simulated, at the left panel with three particles, and at the right with particles subject to a resistory force.
02:03
To construct the path probability, we consider it a discrete Brownian motion, and we use the property of Markov, considering that the system doesn't have a memory. This means that the next step of a particle is not influenced by the previous steps. Associating the probability of the position for each trajectory steps, we obtain the Wiener path integral,
02:26
numbered as equation 2. An important model of path integrals is the functional integral. To obtain it, we can have it in an analog way to the expected value, as we can see in equation 3. Now, to go to the continuous integral,
02:42
we make the number of steps tend to infinity, resulting in the functional integral that we can see in equation 4. To treat the Brownian motion in the observed medium, we use the functional shown as equation 5, in which V is the rate of absorption of the particles in the medium.
03:03
Thus, the path integral remains in the form of equation 6. An important result of this theory is that this path integral satisfies the partial equation 7, known as the Feynman-Kac formula, connecting the stochastic theory with the partial equations theory.
03:21
Now, we use the Feynman-Kac formula to connect the path integral of the Brownian motion with the Feynman path integral to the quantum mechanics. We do so modifying the Feynman-Kac formula to the Schrodinger equation, number 8. Doing so, we can establish a map to modify the Wiener path integral,
03:42
and then we get the Feynman path integral, number 9, which satisfies the Schrodinger equation to quantum mechanics. As we can see, this path integral is proportional to the classical action, as it's known to be. We can conclude that the Brownian motion methods are powerful tools that we can generalize to multiple problems.
04:03
This presentation illustrates an example of a generalistic use of its methods in quantum mechanics. I've identified the importance of the Feynman-Kac formula, which connects two theories, the stochastic theory and the path integral and the partial equation theory, making this approach possible.
04:25
I'd like to acknowledge the Federal University of Calabras and CAPS for sponsoring this research. Thank you for your time and attention. Here are my references.