Differentiation of Vector Fields

In summary, the conversation discusses the relationship between vector fields X and Y, a curve x(t), and an adjoint curve p(t). It is assumed that p(t) satisfies a certain equation and that there exists a condition where the inner product of p(t) and Y(x(t)) is zero. The goal is to show that the inner product of p(t) and the Lie bracket [Y,X](x(t)) is also zero. The proposed solution involves differentiating and simplifying the expression, but it is unclear how to do so without assuming that the vector fields are self-adjoint.
  • #1
Kreizhn
743
1

Homework Statement


Let X,Y be vector fields and x(t) be a curve satisfying
[tex] \dot x(t) = X(x(t)) + u(t) Y(x(t)), u(t) \in \mathbb R [/itex]
and assume there exists p(t) an adjoint curve satisfying
[tex] \dot p(t) = -p(t) \left( \frac{\partial X}{\partial x}(x(t)) + u(t) \frac{\partial Y}{\partial x} (x(t)) \right) [/tex]
If [itex] \langle p(t), Y(x(t)) \rangle = 0 [/itex] show that [itex] \langle p(t), [Y,X](x(t)) \rangle = 0 [/itex]

The Attempt at a Solution


This should be done by differentiating. I get that
[tex] \begin{align*}
\frac{d}{dt} \langle p(t), Y(x(t)) \rangle &= \langle \partial_t p(t), Y(x(t)) \rangle + \langle p(t) \partial_x Y(x(t)) \partial_t x(t) \rangle \\
&= \langle -p \left( \partial_x X +u \partial_x Y\right), Y \rangle + \langle p,\partial_xY ( X + u Y) \rangle \end{align*}
[/tex]

I don't see how this simplifies.

Edit: Where we're taking [itex] [A,B] = (\partial_x A)B - (\partial_x B)A [/itex].
 
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  • #2
It works if the vector fields are somehow self-adjoint, but I have no reason to believe that this should be true.
 

Related to Differentiation of Vector Fields

1. What is the concept of differentiation in vector fields?

Differentiation in vector fields is the process of finding the rate of change or the slope of a vector field at a specific point. It involves calculating the derivative of the vector field, which represents the direction and magnitude of change at that point.

2. Why is differentiation important in vector fields?

Differentiation is important in vector fields because it allows us to understand how the vector field changes over space. It helps us identify critical points, such as maxima and minima, and determine the direction in which the vector field is changing.

3. What are the different methods for differentiating vector fields?

There are two main methods for differentiating vector fields: the gradient method and the directional derivative method. The gradient method involves finding the partial derivatives of each component of the vector field with respect to each independent variable. The directional derivative method involves calculating the dot product between the gradient of the vector field and a unit vector in the direction of interest.

4. Can any vector field be differentiated?

Yes, any vector field can be differentiated as long as it is continuous and differentiable. However, some vector fields may be more difficult to differentiate than others, depending on their complexity and the available methods.

5. How is differentiation of vector fields used in real-world applications?

Differentiation of vector fields is used in various real-world applications, such as physics, engineering, and economics. It can be used to model and predict the behavior of physical systems, optimize processes in engineering, and analyze trends and patterns in economic data.

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