# Tensor Calculus 3: The Jacobian

TLDRThis video delves into the concept of the Jacobian matrix, a fundamental tool for transforming basis vectors between coordinate systems. It builds upon the forward and backward transformations discussed in the 'Tensors for Beginners' series. The video explains how to construct the polar basis vectors from Cartesian ones using partial derivatives of a position vector, showcasing the Jacobian as the forward transform matrix. It also covers the inverse Jacobian, demonstrating that it serves as the backward transform, and proves that the two matrices are indeed inverses of each other. The script concludes by emphasizing the multivariable chain rule's role in deriving the coefficients for these transformations.

###### Takeaways

- π The Jacobian is introduced as a matrix used to transform basis vectors between coordinate systems, serving as an upgrade from the forward and backward transforms discussed in the 'tensors for beginners' series.
- π The video explains the concept of transforming between Cartesian and polar coordinate systems, emphasizing the changing nature of polar basis vectors relative to points in space.
- π The forward transform matrix, F, is constructed using scaling coefficients which are essentially the partial derivatives of a position vector with respect to the coordinate variables.
- π The Jacobian matrix, J, is defined as the matrix of these partial derivatives, which can be used to build a new set of basis vectors from an old set.
- π The basis vector \( \vec{e}_\Theta \) is discussed, with the video noting that it is not normalized, leading to factors of R in the Jacobian matrix entries.
- π The Jacobian matrix is shown to be a versatile tool, capable of generating a forward transform matrix for any point in space by substituting the point's coordinates.
- π The inverse Jacobian matrix is introduced as the backward transform, denoted by \( J^{-1} \), which allows for the construction of Cartesian basis vectors from polar basis vectors.
- π’ The video demonstrates the calculation of the inverse Jacobian matrix by using the multivariable chain rule and partial derivatives, resulting in a matrix that is the inverse of the original Jacobian.
- βοΈ The script proves that the Jacobian and inverse Jacobian matrices are indeed inverses of each other, multiplying to yield the identity matrix.
- π Einstein notation is used to simplify the proof of the inverse relationship between the Jacobian matrices, showing that the Kronecker Delta emerges from their multiplication.
- π The takeaway is that the Jacobian matrix represents the forward transform, while its inverse represents the backward transform, with coefficients derived from the multivariable chain rule.

###### Q & A

### What is the Jacobian matrix used for?

-The Jacobian matrix is used for transforming basis vectors between coordinate systems. It is essentially an upgraded version of the forward and backward transforms that allow for the conversion of one set of basis vectors into another using scaling coefficients.

### Why is the Jacobian matrix significant in the context of polar and Cartesian coordinate systems?

-The Jacobian matrix is significant because it provides a way to build the polar basis vectors, which change direction and length from point to point, out of the Cartesian basis vectors using the multivariable chain rule.

### What is the role of partial derivatives in the construction of the Jacobian matrix?

-Partial derivatives are the coefficients used in the Jacobian matrix that represent the relationship between the coordinate variables. They are used to transform between the basis vectors of different coordinate systems.

### How does the basis vector E Theta differ in the script's explanation from a normalized version?

-In the script, E Theta is not normalized, meaning it grows in size as we move farther from the origin, resulting in factors of R in the equations. Some textbooks normalize E Theta so that it always has a length of one, which eliminates the extra factors of R.

### What is the difference between the forward transform matrix F and the Jacobian matrix J?

-The forward transform matrix F and the Jacobian matrix J are essentially the same. The Jacobian matrix is another term for the forward transform matrix that contains the coefficients needed to build a new set of basis vectors using an old set.

### Can you provide an example of how the Jacobian matrix is used to transform coordinates?

-Yes, the script provides examples where a point with Cartesian coordinates (1,1) is transformed into polar coordinates using the Jacobian matrix, resulting in polar coordinates with R equal to the square root of two and Theta equal to Pi over four radians.

### What is the inverse Jacobian matrix, and how is it related to the Jacobian matrix?

-The inverse Jacobian matrix is the matrix of partial derivatives that gives the coefficients needed to build the Cartesian basis vectors out of the polar basis vectors. It is denoted by J with a minus one exponent and is the inverse of the Jacobian matrix.

### How can one prove that the Jacobian matrix and its inverse are indeed inverses of each other?

-One can prove this by multiplying the Jacobian matrix by its inverse and showing that the result is the identity matrix. This can be done using standard matrix multiplication rules and the multivariable chain rule.

### What is the Einstein notation, and how is it used in the script to simplify the proof of the Jacobian matrix's properties?

-The Einstein notation is a convention used in the script to simplify the proof of the Jacobian matrix's properties by using implicit summation over repeated indices. It allows for a more compact representation of the multivariable chain rule and the Kronecker Delta, which are essential in proving the inverse relationship between the Jacobian and its inverse.

### What is the main takeaway from the video script regarding the Jacobian matrix?

-The main takeaway is that the Jacobian matrix represents the forward transform, and its inverse represents the backward transform. The coefficients for both are derived using the multivariable chain rule, and they can be applied to any number of dimensions.

###### Outlines

##### π Introduction to the Jacobian Matrix

The first paragraph introduces the concept of the Jacobian matrix, which is essential for transforming basis vectors between different coordinate systems. It builds upon the concept of forward and backward transforms from the 'Tensors for Beginners' series. The video explains how to construct new basis vectors from old ones using scaling coefficients in a matrix called the forward transform 'F'. The example of Cartesian and polar coordinate systems is used to illustrate the process. The polar system's changing basis vectors are addressed by using the multivariable chain rule and partial derivatives of a position vector 'R'. The resulting matrix of partial derivatives is identified as the Jacobian matrix, which serves as the forward transform matrix, with the coefficients representing the transformation from Cartesian to polar basis vectors.

##### π Exploring Forward and Backward Transforms with the Jacobian

The second paragraph delves deeper into the application of the Jacobian matrix for forward and backward transformations. It demonstrates how to use the Jacobian matrix to create a forward transformation matrix for any point in space by substituting the coordinates into the matrix. The inverse Jacobian matrix is introduced as the backward transform, which allows for the construction of Cartesian basis vectors from polar basis vectors. The paragraph explains the process of deriving these transformations using partial derivatives and the multivariable chain rule. It also discusses the proof that the Jacobian and its inverse are indeed reciprocal to each other, resulting in the identity matrix when multiplied, which is shown using both standard matrix multiplication and Einstein notation.

##### π§© The Jacobian and Its Inverse: Understanding Multivariable Transforms

The final paragraph summarizes the key learnings from the video regarding the Jacobian and its inverse in the context of curvilinear coordinates. It emphasizes that the forward and backward transformation formulas are derived from the multivariable chain rule and can be represented using either partial derivative notation or basis vector notation. The paragraph highlights the ability to generalize these concepts for any number of dimensions and introduces the use of vector notation for clarity. The main takeaway is that the Jacobian represents the forward transform, while its inverse represents the backward transform, with the coefficients obtained through the multivariable chain rule. The video concludes with a preview of the next topic, which will explore why derivatives are vectors and the concept of contravariant components.

###### Mindmap

###### Keywords

##### π‘Jacobian

##### π‘Coordinate Systems

##### π‘Basis Vectors

##### π‘Forward Transform

##### π‘Partial Derivatives

##### π‘Multivariable Chain Rule

##### π‘Polar Coordinates

##### π‘Cartesian Coordinates

##### π‘Inverse Jacobian

##### π‘Einstein Notation

##### π‘Kronecker Delta

###### Highlights

Introduction to the Jacobian, a matrix used for transforming basis vectors between coordinate systems.

Recommendation to watch a previous video on forward and backward transforms for a foundational understanding.

Explanation of how the forward transform matrix (F) is constructed using scaling coefficients.

Discussion on the challenges of transforming basis vectors in the polar coordinate system due to their changing nature.

The revelation that the forward transform for polar coordinates is simplified by considering basis vectors as partial derivatives of a position vector.

Derivation of the partial derivatives for converting between Cartesian and polar coordinates.

Clarification on the unnormalized version of the basis vector E Theta and its implications on the Jacobian matrix.

The Jacobian matrix is identified as both the forward transform matrix and the matrix of partial derivatives.

Illustration of how to use the Jacobian matrix to transform basis vectors at specific points in space.

Examples of applying the Jacobian matrix to points with given Cartesian and polar coordinates.

Demonstration of the inverse Jacobian matrix and its role in transforming from polar to Cartesian basis vectors.

The process of deriving the coefficients for the inverse Jacobian matrix using the multivariable chain rule.

Proof that the Jacobian and inverse Jacobian matrices are indeed inverses of each other.

Use of Einstein notation to simplify the proof of the Jacobian matrices being inverses.

Generalization of the Jacobian and inverse Jacobian matrices for any number of dimensions.

The multivariable chain rule's fundamental role in deriving the coefficients for both forward and backward transforms.

The main takeaway that the Jacobian represents the forward transform and the inverse Jacobian the backward transform.

Anticipation of the next video covering why derivatives are vectors and the concept of contravariant components.

###### Transcripts

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