Tensor/Vector decomposition/representation & DOF arguement

  • #1
binbagsss
1,254
11
In fluid mechanics, it is sometimes useful to present the velocity, ##U## in terms of a scalar potential ##\Phi## as:##\vec{U}=\nabla \phi##
##U## has 3 dof. ##\phi## has 1.If asked why this works, in terms of a dof argument, why is this?e.g . compared to GR common decomposition of the Riemann tensor into##R^{\nu\lambda\kappa\rho} =R*^{\nu\lambda\kappa\rho} + BR\eta*^{\nu\lambda\kappa\rho} + C^{\nu\lambda\kappa\rho},## (1)

With ##C^{\nu\lambda\kappa\rho}## is traceless, ##R=\eta_{\nu\kappa} \eta_{\lambda\rho}R^{\nu\lambda\kappa\rho}##, where #\eta^{ab}# denotes the Minkowski metric and ##B,C## are constants, and where ##R*^{\nu\lambda\kappa\rho}## denotes ##R*^{\nu\lambda\kappa\rho}=\frac{1}{4}\left[{R}^{\nu\kappa}\eta^{\lambda\rho}-R^{\nu\rho}\eta^{\lambda\kappa}+{R}^{\lambda\rho}\eta^{\nu\kappa}-{R}^{\lambda\kappa}\eta^{\nu\rho}\right] ## and## \eta*^{\nu\lambda\kappa\rho}= 2\eta^{\nu\kappa}\eta^{\lambda\rho}-2\eta^{\nu\rho}\eta^{\lambda\kappa} ##

From what I understand the tensors making up the decomposition can be a number of degrees of freedom at most the same as the object they are making up, and altogether they must have at least the total number of dof of the object they are making up. So , e.g. if T=A+B+C, and T has 10 d.o.f, then each of A,B,C can have any number between [1,10] of d.o.f, and altogether they must have at least 10 d.o.f? Is this correct?

So looking at the above, equation (1), the first term has 10 dof, the second 1, and the last 10 dof. Or would you look at the first term as a sum of four terms each of 10 dof, so it could potentially represent a tensor with 40 dof?

Thanks.
 
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  • #2
binbagsss said:
In fluid mechanics, it is sometimes useful to present the velocity, ##U## in terms of a scalar potential ##\Phi## as:##\vec{U}=\nabla \phi##
##U## has 3 dof. ##\phi## has 1.

This is only possible if the flow is irrotational, ie. [itex]\nabla \times \vec U = 0[/itex].
 
  • #3
pasmith said:
This is only possible if the flow is irrotational, ie. [itex]\nabla \times \vec U = 0[/itex].
apologies yes I should have added that.
 

1. What is tensor decomposition?

Tensor decomposition is a mathematical process that breaks down a higher-order tensor into a combination of lower-order tensors. It is used to simplify complex data structures and make it easier to analyze and interpret data.

2. What is vector representation?

Vector representation is a way of representing data or information using vectors. This allows for easier manipulation and analysis of data, as well as providing a more intuitive way of understanding complex data sets.

3. What is the difference between tensor and vector decomposition?

The main difference between tensor and vector decomposition is the dimensionality of the data being decomposed. Tensor decomposition is used for higher-order tensors, while vector decomposition is used for lower-order tensors.

4. What is the DOF argument in tensor and vector decomposition?

The DOF (degrees of freedom) argument in tensor and vector decomposition refers to the number of independent components needed to fully describe a tensor or vector. It is used to determine the minimum number of components needed for an accurate decomposition.

5. How is tensor and vector decomposition used in data analysis?

Tensor and vector decomposition are commonly used in data analysis to simplify and extract useful information from complex data sets. They can help identify patterns, relationships, and trends in data, and can also be used for data compression and feature extraction.

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