Matrix of Gradients: Notation Explained

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In summary, the conversation discusses confusion over the notation used in index and matrix form for the equation dai = aj∇j ui. The individual expresses confusion over the notation and the correct way to rewrite it, considering a matrix of displacement gradients and a row vector. The expert summarizes that the end result is the same, but there may be differences in notation between physics and math texts. The conversation ends with a clarification on how to write the dot product as a scalar in index notation.
  • #1
aaaa202
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There is one point in my book, where I am confused about the notation. In index notation the equation is:

dai = ajj ui

In matrix notation I would write this as:

da = (a⋅∇)u

where the term in the parenthis is just a scalar or if you will the unit matrix multiplied by a scalar.

But my book rewrites this as:

da = a ⋅ ∇u (1)

where the latter is a matrix of gradients with elements Aij = ∇jui

I don't understand this last rewriting. If you choose to use this matrix of gradients shouldn't it be:

da = (∇u)a

Or maybe I'm misinterpreting (1). Isn't a in this case a row vector and the matrix of displacement gradients has for example on the first row: ∇xux,∇yux ,∇zux. I would like it to be transposed to make meaning of the above.
 
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  • #2
Just so I know we are speaking the same language:
aaaa202 said:
There is one point in my book, where I am confused about the notation. In index notation the equation is:

dai = ajj ui
i.e. ##\begin{pmatrix} da_1\\ da_2 \\ da_3 \end{pmatrix} =\begin{pmatrix} a_1 \frac{\partial u_1}{\partial x_1} &a_2 \frac{\partial u_1}{\partial x_2}& a_3 \frac{\partial u_1}{\partial x_3} \\
a_1 \frac{\partial u_1}{\partial x_1} &a_2 \frac{\partial u_2}{\partial x_2}& a_3 \frac{\partial u_2}{\partial x_3} \\
a_1 \frac{\partial u_3}{\partial x_1} &a_2 \frac{\partial u_3}{\partial x_2}& a_3 \frac{\partial u_3}{\partial x_3} \end{pmatrix} ##

In matrix notation I would write this as:

da = (a⋅∇)u

where the term in the parenthis is just a scalar or if you will the unit matrix multiplied by a scalar.

How exactly would you define ## (a \cdot \nabla)## as a scalar?
But my book rewrites this as:

da = a ⋅ ∇u (1)

where the latter is a matrix of gradients with elements Aij = ∇jui

I don't understand this last rewriting. If you choose to use this matrix of gradients shouldn't it be:

da = (∇u)a

Or maybe I'm misinterpreting (1). Isn't a in this case a row vector and the matrix of displacement gradients has for example on the first row: ∇xux,∇yux ,∇zux. I would like it to be transposed to make meaning of the above.

You are saying that ##\nabla u =
\begin{pmatrix} \frac{\partial u_1}{\partial x_1} & \frac{\partial u_1}{\partial x_2}& \frac{\partial u_1}{\partial x_3} \\
\frac{\partial u_1}{\partial x_1} & \frac{\partial u_2}{\partial x_2}& \frac{\partial u_2}{\partial x_3} \\
\frac{\partial u_3}{\partial x_1} & \frac{\partial u_3}{\partial x_2}& \frac{\partial u_3}{\partial x_3} \end{pmatrix} ##
and ##a = \begin{pmatrix} a_1 & a_2 & a_3 \end{pmatrix} ##
So multiplying would have to be done by ##a^T##, but that would give you a 3x1 matrix out, and appears to be equivalent to:
## \begin{pmatrix} da_1\\ da_2 \\ da_3 \end{pmatrix} = \begin{pmatrix} a_1 \frac{\partial u_1}{\partial x_1} + a_2 \frac{\partial u_1}{\partial x_2} + a_3 \frac{\partial u_1}{\partial x_3} \\
a_1 \frac{\partial u_1}{\partial x_1} + a_2 \frac{\partial u_2}{\partial x_2}+ a_3 \frac{\partial u_2}{\partial x_3} \\
a_1 \frac{\partial u_3}{\partial x_1} + a_2 \frac{\partial u_3}{\partial x_2}+ a_3 \frac{\partial u_3}{\partial x_3} \end{pmatrix} ##

In general, matrix notation is flexible as long as you make sure your dimensions match with the operation you are trying to use. Most physics texts love to vector operations whereas many math and stats texts multiply by transpose matrices. The end result is the same.
 
  • #3
I agree with the last result is what I want to get. But does that match with what you get in (1)? If I multiply the row vector a from the right with the matrix of gradients you have written, I don't get the last matrix in the above. Do you? Maybe I am simply failing to multiply the row vector by a matrix.
Also I would write the dot product as a scalar in index notation as ajj
 

Related to Matrix of Gradients: Notation Explained

1. What is a matrix of gradients?

A matrix of gradients is a mathematical representation of a vector field, where each element in the matrix represents the rate of change of a specific variable at a particular point in space. It is commonly used in fields such as physics, engineering, and computer science to analyze and visualize complex systems.

2. What does the notation in a matrix of gradients mean?

The notation in a matrix of gradients typically consists of partial derivative symbols (∂) and variables (x, y, z) arranged in a grid format. Each element in the matrix represents the partial derivative of a variable with respect to another variable. For example, the element in the first row and second column would represent the rate of change of the first variable with respect to the second variable.

3. How is a matrix of gradients used in real-world applications?

A matrix of gradients is used in a wide range of real-world applications, such as predicting weather patterns, analyzing fluid flow in pipes, and optimizing machine learning algorithms. It allows scientists and engineers to understand the behavior of complex systems and make informed decisions based on the rate of change of variables.

4. What are the benefits of using a matrix of gradients?

One of the main benefits of using a matrix of gradients is that it allows for a more comprehensive analysis of a system by considering multiple variables and their interactions. It also provides a visual representation of the rate of change of variables, making it easier to identify patterns and trends in data.

5. Are there any limitations to using a matrix of gradients?

While a matrix of gradients is a powerful tool, it does have its limitations. It is most effective when working with continuous and differentiable functions, and may not be as accurate when dealing with discontinuous or non-differentiable functions. Additionally, the interpretation of the results can be complex and may require advanced mathematical knowledge.

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