Nonlinear system of differential equations

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Discussion Overview

The discussion revolves around a nonlinear system of differential equations related to a PhD thesis problem. Participants explore methods for solving the system, including both analytical and numerical approaches, and consider the impact of a stochastic term on the equations.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • One participant presents a system of differential equations and seeks a general solution, considering perturbation theory for small values of a parameter.
  • Another participant suggests using Newton's method for a numerical solution and offers further assistance if needed.
  • A different participant proposes a method to express the relationship between variables using separable equations, although they express uncertainty about incorporating the stochastic term.
  • Another participant mentions that the system can be solved analytically but notes that the solution must be expressed in parametric form due to the complexity of the integrals involved.

Areas of Agreement / Disagreement

Participants present multiple approaches to the problem, with no consensus on a single method or solution. The discussion remains unresolved regarding the best way to incorporate the stochastic term and the feasibility of obtaining a general solution.

Contextual Notes

Participants note that the integrals involved may not be expressible in terms of elementary functions and may require elliptic functions, indicating limitations in the analytical approach.

sith
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Hi! I'm working with my PhD thesis at the moment, and I've stumbled upon a pretty involved problem. What I have is a system of equations like this:

\frac{dx}{dt} = A \cos(z)
\frac{dy}{dt} = B x \frac{dx}{dt}
\frac{dz}{dt} = y

where A and B are constants. I also have a stochastic term to z according to:

\delta z(t) = \lim_{N \rightarrow \infty}\pi\sqrt{\frac{t}{N\tau}}\sum_{i = 1}^{N}\zeta_i

where \zeta_i are random numbers of unit variance (normal distributed probability), and \tau is the time scale for the decorrelation of z. I wish to calculate the variance of x as a result of the stochastic variation of z, i.e.,

\langle(\Delta x - \langle\Delta x\rangle)^2\rangle

where \Delta x = x(\tau) - x(0) and \langle ... \rangle is the average of the expression within the brackets with respect to a variation of the values of \zeta_i, weighted according to their probability. I've already calculated the variance of x for B = 0 for which z = y t + z_0 and dx/dt can simply be integrated in time to obtain an analytical expression for x(t). How can I continue to get a more general solution to the problem? Can I e.g. use some perturbation theory for small values of B to begin with?
 
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If you want a numerical solution, then I would go for Newton's method, send me a message if you want further help.
 
Perhaps the following will be a bit helpful?

\dot{y}/\dot{x} = \frac{dy}{dx} = Bx.

You can then solve for y(x). Similarly,

\dot{z} = \frac{dz}{dt} \frac{1}{\dot{x}} \Rightarrow \frac{dz}{dt} = \dot{x} y(x) = A \cos (z) y(x).

This is a separable equation that you can use to solve for z(x). Once you solve for z(x), you can plug that into your equation for x to get

\frac{dx}{dt} = A\cos z(x),

which is again separable (but you may not be able to express the integral in terms of elementary functions. I haven't tried but I'm guessing the integral to do won't be nice).

I'm not entirely sure how to add in the stochastic term, but hopefully this can get you started with something.
 
The system of 3 EDOs can be analytically solved, but the result has to be expressed on a parametric form, because the last integral cannot be expessed in terms of a finite number of elementary functions. Even in the simplest cases of constants C1 and C2 (nul for example), the integral involves some elliptic functions on a very complicated form.
 

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Oh, this is really great! Thanks everyone for your help :D
 

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