Change of variables in a second order SDE

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The discussion revolves around deriving analytical expressions for the drift and variance in a coupled set of stochastic differential equations (SDEs). The user, Simon, initially seeks guidance on using Itô's lemma to calculate the differentials involved in the SDEs. Progress has been made, revealing that the variance, σ²_v, takes a Lorentzian form under certain conditions, specifically when A is much smaller than n³R/t_c². Simon raises further questions regarding the validity of the Lorentzian approximation, potential phase dependence of σ_v, and methods for deriving σ_v when the initial condition is not met. The conversation highlights the complexities of SDE analysis and the need for numerical simulations to explore drift effects due to stochasticity in φ.
sith
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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

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

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

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

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

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)

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

Thanks in advance. /Simon
 
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I have made some progress in the work. Treating H as constant \sigma_v can be found to be

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

by using Itô's lemma on the more simple form

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

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

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

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

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

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

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