Differential Equation with Noise term

In summary, the conversation discusses a problem involving a homogeneous linear differential equation with an added noise term, which is uncorrelated between times and has a Gaussian distribution with zero mean. The goal is to calculate the time dependence of f(x,t) and the Fourier transform of f(x,t). The speaker suggests using the Green's function, but is unsure of how to proceed without knowing the specific function for the noise. They mention a lack of knowledge about random processes and express a desire to have more time to review relevant coursework.
  • #1
MisterX
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Homework Statement


[tex] (\partial_t - A\nabla^2 + B)f(x) = \eta(x, t) [/tex]
So I have a homogeneous linear differential equation except for an added noise term ##\eta(t) ##. The noise is uncorrelated between times and has a Gaussian distribution with zero mean. That is we have Gaussian white noise.

The problem is to calculate the time dependence of f(x,t) and also the time dependence of ##\left| F(k, t)\right|^2 ##, where F(k, t) is the Fourier transform of f(x, t).


Homework Equations

The Attempt at a Solution



If I know the specific function for the noise, I can solve using the Green's function - but I do not know ##\eta(t) ##.
What can I say about the behavior of the solution? If you like, the noise starts at t=0, and we are interested in some kind of transient response I suppose. I began to proceed with solving the Green's function in Fourier space (that is x -> k, but t remained unchanged) but I am not sure what to do next.

I am wishing I knew or remember more I guess.

 
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  • #3
This is actually for a different course, we haven't been taught at all about random processes. However, years ago when I was getting my bachelors as an engineer I learned a little bit. I'd like to be able to do a nice job with this but I don't have time for a huge amount of extra learning.
 

Related to Differential Equation with Noise term

What is a differential equation with a noise term?

A differential equation with a noise term is a type of differential equation that includes a random or unpredictable element in the form of a noise term. This noise term can represent external factors or fluctuations in the system being modeled, making the solution of the differential equation more realistic and applicable to real-world scenarios.

How is a differential equation with a noise term different from a regular differential equation?

The main difference between a differential equation with a noise term and a regular differential equation is the inclusion of the noise term. In a regular differential equation, the solution is deterministic and does not consider external factors. In contrast, a differential equation with a noise term takes into account the random element of the system, making the solution more probabilistic in nature.

What are some real-life applications of differential equations with noise terms?

Differential equations with noise terms are commonly used in fields such as physics, chemistry, biology, finance, and engineering to model systems that are affected by random or unpredictable factors. For example, they can be used to model the movement of particles in a fluid, the spread of diseases in a population, or the fluctuations in stock prices.

What methods are used to solve differential equations with noise terms?

There are several methods for solving differential equations with noise terms, including numerical methods, stochastic calculus, and perturbation methods. These methods take into account the random nature of the noise term and provide approximate solutions that can be used for analysis and prediction of the system's behavior.

Are there any challenges associated with solving differential equations with noise terms?

Yes, there are several challenges associated with solving differential equations with noise terms. One of the main challenges is the difficulty in accurately modeling the noise term, as it is often based on complex and unpredictable factors. Additionally, the solutions obtained from these equations may not be exact, but rather probabilistic in nature, making it challenging to make precise predictions about the system's behavior.

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