Construction of metric from tensor products of vectors

In summary, the metric ##g_{\mu \nu}## of spacetime is constructed from tensor products of vectors, such as the unit vectors in the respective directions. One vector, named ##A##, is used. The equation ##g_{\mu \nu} = \lambda \frac{A_\mu A_\nu}{g^{\alpha \beta} A_\alpha A_\beta} + ...## is relevant, where neither ##\lambda## nor the further terms involve ##A##. When ##g## is differentiated with respect to ##A##, the factor ##A_\alpha A_\beta \frac{\partial g^{\alpha\beta}}{\partial A_\tau}## appears from differentiating the
  • #1
gerald V
67
3
1.
The metric ##g_{\mu \nu}## of spacetime shall be constructed from tensor products of vectors (relevant are the unit vectors in the respective directions). One such vector shall be called ##A##.

Homework Equations


##g_{\mu \nu} = \lambda \frac{A_\mu A_\nu}{g^{\alpha \beta} A_\alpha A_\beta} + ...##, where neither ##\lambda## nor the further terms shall involve ##A##. Now ##g## shall be differentiated w.r.t. ##A##, this is ##\frac{\partial g_{\mu\nu}}{\partial A_\tau}## .[/B]

The Attempt at a Solution


Everything works fine except one term originating from differentiating the denominator. It contains a factor ##A_\alpha A_\beta \frac{\partial g^{\alpha\beta}}{\partial A_\tau}##. I have no idea what to do with this expression. Is it zero? Or is it equal to to ##A^\alpha A^\beta \frac{\partial g_{\alpha\beta}}{\partial A_\tau}##? Or what else?

Many thanks for any advice.
 
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  • #2
gerald V said:
It contains a factor ##A_\alpha A_\beta \frac{\partial g^{\alpha\beta}}{\partial A_\tau}##. I have no idea what to do with this expression.

To find an expression for this factor, contact ##g_{\mu \nu} = \lambda \frac{A_\mu A_\nu}{g^{\alpha \beta} A_\alpha A_\beta} + ...## with ##A^\mu A ^\nu##, and take the derivative with respect to ##A_\tau##.
 
  • #3
gerald V said:
It contains a factor ##A_\alpha A_\beta \frac{\partial g^{\alpha\beta}}{\partial A_\tau}##. I have no idea what to do with this expression. Is it zero? Or is it equal to to ##A^\alpha A^\beta \frac{\partial g_{\alpha\beta}}{\partial A_\tau}##? Or what else?

Actually, it might be better to look at
$$0 = \frac{\partial}{\partial A_\tau} \left( \delta^\alpha_\beta \right) .$$
 

FAQ: Construction of metric from tensor products of vectors

What is the purpose of constructing a metric from tensor products of vectors?

The purpose of constructing a metric from tensor products of vectors is to define a mathematical structure that allows for the calculation of distances and angles between vectors in a multi-dimensional space. This is useful in various fields of science, including physics, engineering, and computer science.

How is a metric constructed from tensor products of vectors?

A metric is constructed from tensor products of vectors by taking the tensor product of two vectors and then taking the inner product of the resulting tensor with itself. This process results in a scalar value that represents the "length" of the tensor, which can then be used as a distance measure.

What are the properties of a metric constructed from tensor products of vectors?

A metric constructed from tensor products of vectors has the following properties: symmetry (the order of the vectors in the tensor product does not matter), positive-definiteness (the metric is always positive or zero), and linearity (the metric follows the distributive property). These properties allow for the consistent calculation of distances and angles between vectors.

Can a metric constructed from tensor products of vectors be applied to non-Euclidean spaces?

Yes, a metric constructed from tensor products of vectors can be applied to non-Euclidean spaces. This is because the metric is defined in terms of the tensor product and inner product, which are both general mathematical concepts that can be applied to any type of space.

How is a metric constructed from tensor products of vectors used in practical applications?

A metric constructed from tensor products of vectors is used in practical applications, such as machine learning and computer graphics, to measure the similarity or dissimilarity between vectors. It is also used in physics to calculate the distances and angles between vectors in multi-dimensional spaces, such as in general relativity and quantum mechanics.

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