What is the solution to this ODE (and SDE)?

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In summary, we are given an Ito stochastic differential equation with the L^2 norm and standard Wiener process. We are unsure if there is an analytical solution, but we hope to find one for the expected value E[X_t]. To better understand the problem, we are considering a deterministic ordinary differential equation. The solution is most easily found in spherical polar coordinates, but finding a representation valid for all time may be impossible due to the angular components becoming invalid if ever \theta(t) < 0 or \theta(t) > \pi.
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Only a Mirage
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I'm trying to analyze the following Ito stochastic differential equation:

$$dX_t = \|X_t\|dW_t$$

where [itex]X_t, dX_t, W_t, dW_t \in \mathbb{R}^n[/itex]. Here, [itex]dW_t[/itex] is the standard Wiener process and [itex]\|\bullet\|[/itex] is the [itex]L^2[/itex] norm. I'm not sure if this has an analytical solution, but I am hoping to at least find an analytical expression for the expected value [itex]E[X_t][/itex].

In order to gain intuition for this problem, I'm considering the following ordinary differential equation:

$$\dot{z}(t) =\|z(t)\|b(t)$$

where [itex]z(t), b(t) \in \mathbb{R}^n [/itex] and everything is completely deterministic. Does anyone know the analytical solution to this second equation, and under what conditions it exists?
 
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  • #2
Only a Mirage said:
I'm trying to analyze the following Ito stochastic differential equation:

$$dX_t = \|X_t\|dW_t$$

where [itex]X_t, dX_t, W_t, dW_t \in \mathbb{R}^n[/itex]. Here, [itex]dW_t[/itex] is the standard Wiener process and [itex]\|\bullet\|[/itex] is the [itex]L^2[/itex] norm. I'm not sure if this has an analytical solution, but I am hoping to at least find an analytical expression for the expected value [itex]E[X_t][/itex].

In order to gain intuition for this problem, I'm considering the following ordinary differential equation:

$$\dot{z}(t) =\|z(t)\|b(t)$$

where [itex]z(t), b(t) \in \mathbb{R}^n [/itex] and everything is completely deterministic. Does anyone know the analytical solution to this second equation, and under what conditions it exists?

It is most easily solved in spherical polar coordinates where [itex]\|z\| = r[/itex] so that [itex]z = r\mathbf{e}_r[/itex] and [itex]\mathbf{b} = b_r\mathbf{e}_r + \mathbf{b}_n[/itex] where [itex]\mathbf{b}_n \cdot \mathbf{e}_r = 0[/itex]. We then have [tex]
\dot r \mathbf{e}_r + r \dot{\mathbf{e}_r} = r(b_r\mathbf{e}_r + \mathbf{b}_n).[/tex] Since [itex]\mathbf{e}_r[/itex] and [itex]\dot{\mathbf{e}_r}[/itex] are orthogonal we have [tex]
\dot r = rb_r(t), \\
\dot{\mathbf{e}_r} = \mathbf{b}_n.
[/tex] The radial and angular components thus decouple and the radial component has solution [tex]
r(t) = r(0) \exp\left( \int_0^t b_r(s)\,ds\right).
[/tex] The angular component represents the motion of a point on the unit [itex](n-1)[/itex]-sphere. If [itex]\mathbf{b}[/itex] is continuous then a solution should exist, but finding a co-ordinate representation of it valid for all time may be impossible, as one can see from the angular components in [itex]\mathbb{R}^3[/itex], [tex]
\dot \theta = b_\theta(t), \\
\dot \phi = \frac{b_\phi(t)}{\sin \theta(t)}
[/tex] with solution [tex]
\theta(t) = \theta(0) + \int_0^t b_\theta(s)\,ds, \\
\phi(t) = \phi(0) + \int_0^t \frac{b_\phi(s)}{\sin \theta(s)}\,ds,[/tex] which becomes invalid if ever [itex]\theta(t) < 0[/itex] or [itex]\theta(t) > \pi[/itex].
 
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  • #3
pasmith said:
It is most easily solved in spherical polar coordinates where [itex]\|z\| = r[/itex] so that [itex]z = r\mathbf{e}_r[/itex] and [itex]\mathbf{b} = b_r\mathbf{e}_r + \mathbf{b}_n[/itex] where [itex]\mathbf{b}_n \cdot \mathbf{e}_r = 0[/itex]. We then have [tex]
\dot r \mathbf{e}_r + r \dot{\mathbf{e}_r} = r(b_r\mathbf{e}_r + \mathbf{b}_n).[/tex] Since [itex]\mathbf{e}_r[/itex] and [itex]\dot{\mathbf{e}_r}[/itex] are orthogonal we have [tex]
\dot r = rb_r(t), \\
\dot{\mathbf{e}_r} = \mathbf{b}_n.
[/tex] The radial and angular components thus decouple and the radial component has solution [tex]
r(t) = r(0) \exp\left( \int_0^t b_r(s)\,ds\right).
[/tex] The angular component represents the motion of a point on the unit [itex](n-1)[/itex]-sphere. If [itex]\mathbf{b}[/itex] is continuous then a solution should exist, but finding a co-ordinate representation of it valid for all time may be impossible, as one can see from the angular components in [itex]\mathbb{R}^3[/itex], [tex]
\dot \theta = b_\theta(t), \\
\dot \phi = \frac{b_\phi(t)}{\sin \theta(t)}
[/tex] with solution [tex]
\theta(t) = \theta(0) + \int_0^t b_\theta(s)\,ds, \\
\phi(t) = \phi(0) + \int_0^t \frac{b_\phi(s)}{\sin \theta(s)}\,ds,[/tex] which becomes invalid if ever [itex]\theta(t) < 0[/itex] or [itex]\theta(t) > \pi[/itex].

Thanks a lot for the answer. Can you explain why [tex]
\theta(t) = \theta(0) + \int_0^t b_\theta(s)\,ds, \\
\phi(t) = \phi(0) + \int_0^t \frac{b_\phi(s)}{\sin \theta(s)}\,ds,[/tex] becomes invalid if
ever [itex]\theta(t) < 0[/itex] or [itex]\theta(t) > \pi[/itex]?
 

What is an ODE?

An ODE, or ordinary differential equation, is a type of mathematical equation that describes how one variable changes in relation to another variable. It involves finding a function that satisfies the equation and can be used to model many real-world systems.

What is an SDE?

An SDE, or stochastic differential equation, is a type of differential equation that includes random elements. Unlike ODEs, which have a deterministic solution, SDEs have a probabilistic solution. They are often used to model systems with uncertainty or randomness.

What are the steps to solving an ODE?

To solve an ODE, you first need to identify the type of equation (e.g. linear or nonlinear), then apply the appropriate methods (e.g. separation of variables, substitution, or integrating factors). You may also need to use initial conditions or boundary conditions to find the specific solution.

What are the methods for solving an SDE?

There are several methods for solving SDEs, including the Euler-Maruyama method, Milstein method, and Runge-Kutta method. These methods involve approximating the solution at discrete time intervals and using stochastic calculus to incorporate the random elements.

What are some real-world applications of ODEs and SDEs?

ODEs and SDEs are used in many fields, including physics, biology, economics, and engineering. They can be used to model population growth, chemical reactions, financial markets, and many other systems. They are also used in machine learning and artificial intelligence to make predictions and analyze data.

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