Calculating Jacobian Determinant

In summary, the conversation discusses the Jacobian matrix and its role in determining the functional dependence of fluid elements at different times. The matrix is represented by the terms ##\frac{\partial f_i}{\partial x_i}## and its determinant, when multiplied by ##d^3X##, gives ##d^3x##. The conversation also mentions the importance of avoiding equations with more than two equal indices.
  • #1
Apashanka
429
15
I came across a line in this paper at page (2) at right side 2nd para where it is written ##d^3x=Jd^3X## where ##J## is the Jacobian and x and X are the positions of the fluid elements at time ##t_0## and ##t## respectively.
Here what I have concluded that ##x_i=f(X_i)## where the functional dependence of ##X_i##(e.g ##f(X_i)##) varies with the time evolution of ##x## for the ##i^{th}## coordinate and call this ##f_i## and now using this ##dx_i=\frac{\partial f_i}{\partial X_i}dX_i## and similarly for the ##i^{th},j^{th}## and ##k^{th}##.
The matrix containing the terms ##\frac{\partial f_i}{\partial x_i}## at the diagonal is the Jacobian matrix and it's determinant times ##d^3X## gives ##d^3x##
Isn't it??
 

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  • #2
It's the Jacobian. Your notation is very dangerous. There must never ever be equations with more than two equal indices. If there are two equal indices you have to sum over them, i.e.,
$$\mathrm{d} x_i = \frac{\partial x_i}{\partial X_j} \mathrm{d} X_j=\sum_{j=1}^3\frac{\partial x_i}{\partial X_j} \mathrm{d} X_j ,$$
and the Jacobian is the full determinand of the Jacobian Matrix,
$$J=\mathrm{det} \frac{\partial x_i}{\partial X_j}.$$
By the way, you should not post copyrighted material as an attachement!
 

Related to Calculating Jacobian Determinant

1. What is the Jacobian determinant?

The Jacobian determinant is a mathematical concept used in multivariable calculus to calculate the change in variables from one coordinate system to another. It is represented by a matrix of partial derivatives and is used to determine the scaling factor between two coordinate systems.

2. Why is calculating the Jacobian determinant important?

Calculating the Jacobian determinant is important because it allows us to transform integrals from one coordinate system to another, making it easier to solve complex problems in mathematics, physics, and engineering. It is also used in optimization and machine learning algorithms.

3. How do you calculate the Jacobian determinant?

The Jacobian determinant is calculated by taking the determinant of the Jacobian matrix, which is a matrix of partial derivatives of one set of variables with respect to another set of variables. The formula for the Jacobian determinant is:

|J| = ∂(x,y)/∂(u,v) = ∂x/∂u * ∂y/∂v - ∂x/∂v * ∂y/∂u

4. What are some applications of the Jacobian determinant?

The Jacobian determinant has many applications in mathematics and other fields. It is used in calculating line integrals, surface integrals, and volume integrals in multivariable calculus. It is also used in physics to calculate flux, work, and other quantities in vector fields. In engineering, it is used in optimization and control systems. In machine learning, it is used in gradient descent algorithms and neural networks.

5. Are there any common mistakes when calculating the Jacobian determinant?

Yes, there are a few common mistakes that can occur when calculating the Jacobian determinant. One mistake is forgetting to include the absolute value when taking the determinant of the Jacobian matrix. Another mistake is mixing up the order of the variables in the partial derivatives. It is important to double-check the formula and make sure all variables are in the correct order before calculating the Jacobian determinant.

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