Understanding the Tensor Product of Two One-Forms in Differential Geometry

In summary, the definition for the tensor product of two one-forms \alpha and \beta in V* is (\alpha\otimes\beta)(\mathbf{v},\mathbf{w})=\alpha (\mathbf{v})\beta (\mathbf{w}), where \mathbf{v} and \mathbf{w} are in V. This can be written in terms of coordinate bases as (dx^{\mu}\otimes dx^{\nu})(\mathbf{v},\mathbf{w})=dx^{\mu}(\mathbf{v})dx^{\nu}(\mathbf{w})=V^{\mu}W^{\nu}, and if \
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"Don't panic!"
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I'm relatively new to differential geometry and would like to check that this is the correct definition for the tensor product of (for simplicity) two one-forms [itex]\alpha,\;\beta\;\;\in V^{\ast} [/itex]: [tex](\alpha\otimes\beta)(\mathbf{v},\mathbf{w})=\alpha (\mathbf{v})\beta (\mathbf{w})[/tex] where [itex]\alpha\otimes\beta\;\;\in V^{\ast}\otimes V^{\ast}[/itex] and [itex]\mathbf{v},\;\mathbf{w}\;\;\in V[/itex].
Given this, is it correct to write, [tex](dx^{\mu}\otimes dx^{\nu})(\mathbf{v},\mathbf{w})=dx^{\mu}(\mathbf{v})dx^{\nu}(\mathbf{w})=V^{\mu}W^{\nu}[/tex]
where we have expressed [itex]\mathbf{v}=V^{\mu}\partial_{\mu}[/itex] and [itex]\mathbf{w}=W^{\nu}\partial_{\nu}[/itex] in terms of a coordinate basis [itex]\lbrace\partial_{\mu}\rbrace[/itex] for [itex]V[/itex], and [itex]\lbrace dx^{\mu}\otimes dx^{\nu}\rbrace[/itex] is a coordinate basis for [itex]V^{\ast}\otimes V^{\ast}[/itex] (with [itex]\lbrace dx^{\mu}\rbrace[/itex] a basis for [itex]V^{\ast}[/itex]). As such, if we express [itex]\alpha[/itex] and [itex]\beta[/itex] in terms of the coordinate basis [itex]\lbrace dx^{\mu}\rbrace[/itex] as [itex]\alpha = \alpha_{\mu}dx^{\mu}[/itex] and [itex]\beta = \beta_{\nu}dx^{\nu}[/itex], respectively, we have [tex](\alpha\otimes\beta)(\mathbf{v},\mathbf{w})=\alpha (\mathbf{v})\beta (\mathbf{w})=\alpha_{\mu}\beta_{\nu}dx^{\mu}\otimes dx^{\nu}(\mathbf{v},\mathbf{w})=\alpha_{\mu}\beta_{\nu}V^{\mu}W^{\nu}[/tex].

Would this be correct at all?
 
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  • #2
"Don't panic!" said:
Would this be correct at all?
Yes.
 
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  • #3
Excellent, thanks.
 

What is a tensor product?

A tensor product is a mathematical operation that combines two vectors or matrices into a single tensor. It is commonly used in linear algebra and multilinear algebra.

What is the purpose of a tensor product?

The purpose of a tensor product is to create a new tensor that captures the relationship between two existing tensors. It allows for the manipulation and analysis of complex multidimensional data.

How is a tensor product calculated?

The tensor product is calculated by taking the outer product of two vectors or matrices and then summing the resulting terms. This results in a new tensor with a higher dimensionality than the original tensors.

What are the properties of a tensor product?

The properties of a tensor product include linearity, associativity, and distributivity. It is also commutative for certain types of tensors, such as symmetric tensors.

What are some real-world applications of tensor products?

Tensor products are commonly used in physics, engineering, and computer science. They are used in fields such as computer vision, machine learning, and fluid dynamics to model and analyze complex systems.

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