Change of variables in a second order SDE

In summary, the conversation discusses a coupled set of stochastic differential equations (SDEs) and the question of deriving analytical expressions for the drift and variance in one of the variables, v, due to the stochasticity in the other variable, \phi. The approach suggested is to use Itô's lemma and treat the constant of motion, H, as a constant. The conversation also mentions a mistake in a previous post regarding the form of \sigma_v, which is actually \sigma^2_v and takes on a Lorentzian approximation in certain limits. The remaining questions include how to derive the Lorentzian form of \sigma_v^2, if it is valid, the possible phase dependence of \sigma_v, and how to
  • #1
sith
14
0
Hello everyone! I am fairly new to SDE theory, so I'm sorry if my question may be a bit naive. I have the following coupled set of SDE:s

[itex]d\phi = \frac{v - v_r}{R}d t + \frac{\pi}{\sqrt{t_c}}d W[/itex]
[itex]d v = A\cos(n\phi - \phi_w)d t + a_v d t + \sigma_v d W[/itex].

[itex]W[/itex] denotes a Wiener process, and the parameters [itex]v_r[/itex], [itex]R[/itex], [itex]t_c[/itex], [itex]A[/itex] and [itex]\phi_w[/itex] are constants. The functions [itex]a_v[/itex] and [itex]\sigma_v[/itex] are the drift and variance in [itex]v[/itex], respectively, solely due to the stochasticity in [itex]\phi[/itex]. So my question is: how do I derive analytical expressions for [itex]a_v[/itex] and [itex]\sigma_v[/itex]? I don't know if this helps, but when disregarding the stochastic processes in [itex]\phi[/itex] and [itex]v[/itex] it will turn into a second order ODE, and one will have the following constant of motion

[itex]H(\phi,v) = \frac{v^2 - 2 v_r v}{2 R} - \frac{A}{n}\sin(n\phi - \phi_w)[/itex].

My first thought of how to solve the problem was to rewrite the expression as [itex]v(\phi, H)[/itex] and using Itô's lemma

[itex]d v = \frac{\partial v}{\partial\phi}d\phi + \frac{\partial v}{\partial H}d H + \frac{1}{2}\left(\frac{\partial^2 v}{\partial\phi^2}d[\phi,\phi] + \frac{\partial^2 v}{\partial\phi\partial H}d[\phi,H] + \frac{\partial^2 v}{\partial H^2}d[H,H]\right)[/itex]

where [itex]d[X,Y][/itex] is the quadratic co-/variance. Is this a correct approach? Then how do I calculate the differentials [itex]d H[/itex], [itex]d[\phi,H][/itex] and [itex]d[H,H][/itex] for this particular case? Numerical simulations have indicated that [itex]\sigma_v[/itex] is on the form of a Lorentzian function in [itex]v[/itex], centered around [itex]v_r[/itex].

Thanks in advance. /Simon
 
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  • #2
I have made some progress in the work. Treating [itex]H[/itex] as constant [itex]\sigma_v[/itex] can be found to be

[itex]\sigma_v = \frac{\pi A R}{\sqrt{t_c}(v - v_r)}\cos(n\phi - \phi_w)[/itex]

by using Itô's lemma on the more simple form

[itex]d v = \frac{d v}{d\phi}d\phi + \frac{1}{2}\frac{d^2 v}{d\phi^2}d[\phi,\phi][/itex].

I also made a mistake in the previous post. It is not [itex]\sigma_v[/itex] that takes the form of a Lorentzian, but [itex]\sigma^2_v[/itex]. Specifically I have found that the Lorentzian approximation is valid in the limit [itex]A \ll \frac{n^3 R}{t_c^2}[/itex], and it then takes the form

[itex]\sigma_v^2 = \frac{\pi^2 A^2 R^2}{2 t_c[v_B^2 + (v - v_r)^2]}[/itex]
[itex]v_B = \frac{\alpha n R}{t_c}[/itex],

where [itex]\alpha \approx 4.92[/itex] is a numerical constant. This expression is consistent with the one derived when assuming [itex]H[/itex] is constant in the limit [itex]|v - v_r| \gg v_B[/itex], and averaging the expression over phase [itex]\phi[/itex]. The questions I am still left with are:

* How do I derive the Lorentzian form of [itex]\sigma_v^2[/itex], if it is even valid?
* Is there a phase dependence in [itex]\sigma_v[/itex]? (There are no indications on a phase dependence from numerical simulations)
* Is there a way to derive [itex]\sigma_v[/itex] when the condition [itex]A \ll \frac{n^3 R}{t_c^2}[/itex] is violated?

Of course I am also interested in estimating the drifts due to stochasticity in [itex]\phi[/itex], which I haven't even begun to look at in numerical simulations.
 

What is a second order SDE?

A second order stochastic differential equation (SDE) is a mathematical equation that describes the evolution of a system over time, taking into account both random and deterministic forces. It is a type of differential equation that has both a first derivative and a second derivative with respect to time.

What is a change of variables?

A change of variables is a mathematical technique used to simplify an equation or problem by substituting one set of variables for another set. This is often done to make the problem easier to solve or understand.

Why is a change of variables useful in second order SDEs?

A change of variables can be useful in second order SDEs because it can transform a complex equation into a simpler one, making it easier to analyze and solve. It can also help to reveal underlying patterns or relationships in the system.

How do you perform a change of variables in a second order SDE?

To perform a change of variables in a second order SDE, you first need to identify the variables that you want to change and the new variables you want to use. Then, you can use substitution or transformation techniques to rewrite the equation in terms of the new variables.

What are some common applications of change of variables in second order SDEs?

Change of variables in second order SDEs is commonly used in fields such as physics, finance, and engineering to simplify and analyze complex systems. It can also be used in stochastic calculus and probability theory to study the behavior of random processes.

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