Trace - Integration - Average - Tensor Calculus

In summary, the dipole-dipole Hamiltonian in ESR is given by H_{DD} = SDS where the anisotropic zero field splitting tensor D and spin S.
  • #1
Joschua_S
11
0
Hi

Let [itex] D [/itex] be an anisotropic tensor. This means especially, that [itex] D [/itex] is traceless. [itex] \mathrm{tr}(D) = 0 [/itex]

Apply the representating matrix of [itex] D [/itex] to a basis vector [itex] S [/itex], get a new vector and multiply this by dot product to your basis vector. Than you got a scalar function.

Now integrate this function over a symmetric region, for example the n-dimensional unit-sphere or a n-dimensional Cube or something other symmetric.

[itex] \int SDS ~ \mathrm{d} \Omega [/itex]

This integral vanish!

[itex] \int SDS ~ \mathrm{d} \Omega = 0[/itex]

My question:

Trace is for me like an average of something. The symmetric integration vanish also, like the trace.

Is there a link between trace zero and the vanishing integral? What is the math behind this.

If you wish one example. A part of a dipole-dipole coupling Hamiltonian in spectroscopy is given by [itex] H_{DD} = SDS [/itex], with the anisotropic zero field splitting tensor [itex] D[/itex] and spin [itex] S [/itex].

Greetings
 
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  • #2
Joschua_S said:
Is there a link between trace zero and the vanishing integral?

Not in the cases like you have presented. A non-zero scalar function has alway non-zero trace. But the integral can be zero or non zero.
 
  • #3
Hi

This means that the integral vanish here is pure randomness?

There are no theorems in math about anisotropic tensors, trace and integrals? :-(

Greetings
 
  • #4
Joschua_S said:
Hi

This means that the integral vanish here is pure randomness?

In fact I see no reason for your integral to vanish unless you have some additional assumptions (that you did not list) about the dependence of your D and S on space variables.
 
  • #5
The dipole-dipole Hamiltonian in ESR is given by

[itex]H_{DD} = \dfrac{\mu_0}{2h} g_j g_k \mu_b^2 \left( \dfrac{\vec{S}_j \cdot \vec{S}_k}{r^3_{jk}} - \dfrac{3(\vec{S}_j \cdot \vec{r}_{jk}) \cdot (\vec{S}_k \cdot \vec{r}_{jk})}{r^5_{jk}} \right)[/itex]

One can write it as

[itex]H_{DD} = \vec{S} \underline{\underline D} \vec{S}[/itex]

with the traceless symmetric Tensor D that fullfills [itex]\int SDS ~ \mathrm{d} \Omega = 0[/itex]

Do you know something about the math behind this?

Greetings
 
  • #6
arkajad? :blushing:
 

What is "Trace" in Tensor Calculus?

In Tensor Calculus, "Trace" refers to the sum of the diagonal elements of a square matrix. It is denoted by the symbol tr(A) or simply Tr(A). The trace of a matrix is a scalar quantity and is often used in calculations involving tensors and their derivatives.

What does "Integration" mean in Tensor Calculus?

In Tensor Calculus, "Integration" refers to the process of finding the integral of a tensor field over a specified region. This involves summing up the contributions of each element of the tensor field over small intervals. Integration is important in many applications of tensor calculus, such as in physics and engineering.

What is the "Average" of a tensor?

The "Average" of a tensor refers to the mean value of all the elements in the tensor. It is calculated by summing up all the elements and dividing by the total number of elements. The average of a tensor is a useful quantity in many applications, such as in statistics and machine learning.

What is the purpose of Tensor Calculus?

The main purpose of Tensor Calculus is to provide a mathematical framework for understanding and manipulating multidimensional quantities, known as tensors. Tensors are used to describe physical quantities that have both magnitude and direction, and they are essential in many fields such as physics, engineering, and computer graphics.

What are some real-world applications of Tensor Calculus?

Tensor Calculus has many real-world applications, such as in physics for understanding the behavior of objects in space-time, in engineering for analyzing stresses and strains in materials, and in machine learning for processing and analyzing multidimensional data. It is also used in computer graphics for rendering 3D images and in economics for analyzing multivariable systems.

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