Why $H$ is a (1,2) tensor field?

In summary, the conversation discusses the definition of a (1,2) tensor on a manifold, specifically the tensor H(X,Y) = \nabla_X Y - \nabla_X^* Y defined by two vector fields X and Y. The conversation shows that H is a vector-valued 2-form and can also be defined as a mapping from V^* \times V \times V \rightarrow R, where V is a vector space and R is the real numbers. The notation for this mapping is (\lambda, X,Y) \mapsto \langle \lambda, H(X,Y)\rangle and it is equivalent to H:V \times V \rightarrow V. The conversation also discusses the components of H and
  • #1
CAF123
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I have a conceptual question associated with one of the worked examples in my notes. The question is:
'Let ##\nabla## and ##\nabla^*## be connections on a manifold ##M##. Show that ##H(X,Y) = \nabla_X Y - \nabla_X^* Y## where ##X,Y## are vector fields defines a (1,2) tensor on M.

To show it is a tensor, one needs to check linearity of the arguments X and Y. This is clear, but I don't understand why these arguments show that H is necessarily of rank (1,2)? The notation H(X,Y) seems to suggest it is a (1,1) tensor since the map H acts on one covector and one vector argument. The solution says we may define a (1,2) tensor by ##(\lambda, X,Y) \mapsto \langle \lambda, H(X,Y)\rangle##, but I am not sure why it must be (1,2) and why this means H is of rank (1,2)?

Thanks!
 
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  • #2
The inputs are two vector fields, and the output is a vector field. In index notation, that means that two upper index quantities are converted into an upper index quantity. The components of the tensor that does this must thus have two lower indices and one upper.

Another way to view this, because of the antisymmetry on exchange of X and Y, is to say that H is a vector-valued 2-form.

If you define a tensor as a mapping from [itex] T : \underbrace{V \times \cdots \times V}_{\mbox{r factors}} \times \underbrace{ V^* \times \cdots \times V^*}_{\mbox{s factors}} \rightarrow R [/itex], you get equivalent definitions of the same tensor by removing factors of [itex] V \mbox{ and } V^*[/itex] and taking their duals when you place them on the RHS of the arrow. Thus [itex] H: V^* \times V \times V \rightarrow R[/itex] is equivalent to [itex] H:V \times V \rightarrow V[/itex]
 
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  • #3
MarcusAgrippa said:
The inputs are two vector fields, and the output is a vector field. In index notation, that means that two upper index quantities are converted into an upper index quantity. The components of the tensor that does this must thus have two lower indices and one upper.

Another way to view this, because of the antisymmetry on exchange of X and Y, is to say that H is a vector-valued 2-form.

If you define a tensor as a mapping from [itex] T : \underbrace{V \times \cdots \times V}_{\mbox{r factors}} \times \underbrace{ V^* \times \cdots \times V^*}_{\mbox{s factors}} \rightarrow R [/itex], you get equivalent definitions of the same tensor by removing factors of [itex] V \mbox{ and } V^*[/itex] and taking their duals when you place them on the RHS of the arrow. Thus [itex] H: V^* \times V \times V \rightarrow R[/itex] is equivalent to [itex] H:V \times V \rightarrow V[/itex]
Thanks! So can I write the equation ##H(X,Y) \rightarrow \nabla_XY - \nabla_X^* Y## in components as ##H^a_{\,\,bc}X^b Y^c = Z^a - Z^{*\,a}##?

Also, why do we define the mapping to be ##(\lambda, X,Y) \rightarrow \langle \lambda, H(X,Y)\rangle##? As far as I understand the notation ##\langle \lambda, H(X,Y) \rangle## means ##\lambda ( H(X,Y))##. Is it because ##H(X,Y) \in V## and therefore since ##\lambda \in V^*## we have a covector acting on a vector which is in R (a co-vector is a mapping ##\lambda: V \rightarrow \mathbb{R}##, so ##\lambda(H(X,Y)) \in \mathbb{R}##) and hence the mapping ##H: (\lambda, X,Y) \mapsto \langle \lambda, H(X,Y)\rangle## is a mapping ##V^* \times V \times V \rightarrow \mathbb{R}## as you wrote?
 
  • #4
Exactly so.
 

1. Why is $H$ considered a (1,2) tensor field?

$H$ is considered a (1,2) tensor field because it has one upper index and two lower indices, indicating that it can take one vector and two covectors as inputs and produce a scalar value as output. This matches the definition of a (1,2) tensor field.

2. What is the role of $H$ in tensor calculus?

In tensor calculus, $H$ is used to represent the linear transformation between two vector spaces. It allows us to describe and manipulate geometric objects such as vectors and tensors in a coordinate-independent manner, making it a powerful tool in mathematical physics and engineering.

3. How is $H$ related to the metric tensor?

The metric tensor is used to define the inner product between two vectors in a particular coordinate system. $H$ is related to the metric tensor through its components, as it is defined as the contraction of the metric tensor with two covariant indices and one contravariant index.

4. Can $H$ be expressed in different coordinate systems?

Yes, $H$ can be expressed in different coordinate systems. This is because, in tensor calculus, we can transform the components of a tensor using the transformation rules for covariant and contravariant components, ensuring that the tensor itself remains unchanged.

5. What are the physical applications of $H$ as a (1,2) tensor field?

$H$ has various physical applications, such as in general relativity, where it is used to describe the curvature of spacetime. It is also used in continuum mechanics to represent stress and strain tensors, and in fluid dynamics to describe the velocity gradient tensor. Additionally, $H$ is used in electromagnetic theory to describe the electric and magnetic fields.

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