# Tensor basic definition DRAFT NOTES

# Easy but incomplete definition

## I - Tensor = multi-dimensional array of numbers

A tensor is often thought of as a generalized matrix. That is, it could be a 1-D matrix (i.e. a vector), a 3-D matrix (a cube of numbers), a 0-D matrix (a single number), or a higher dimensional structure that is harder to visualize.

The 'n' of n-D in this case is called the order, degree or rank of the tensor. ## Examples of rank 2 tensors

Electromagnetic tensor: Stress tensor: Rank 3, dimension - 3 (i.e 3 elements along the rows and columns) Rank 4 ## Index notation

Each element (number) position in the tensor can be described using indices:

• Rank-0: $$\tau$$, a scalar
• Rank-1: $$\tau_i$$, a vector
• Rank-2: $$\tau_{ij}$$, a matrix (Latin letters for Dimension-2/3)
$$\tau_{\alpha\beta}$$ (Greek letters for Dimension-4)
• Rank-3: $$\tau_{ijk}$$, a cube of matrices.
• Rank-4: $$\tau_{ijkl}$$, an array of cubes
• ...

# II - Augmented array definition

Tensor = an object that is invariant under a change of coordinates and has components that change in a special, predictable way under a change of coordinates. Explanation: Regardless of the coordinate system the pencil always points to the door with the same length. We can transform components with forward or backward transformations. (if the vector/tensor becomes null under specific coordinate systems then it means it's a pseudo-vector/tensor)

## III - Descriptive definition:

A rank-n tensor in m-dimensions is a mathematical object that has n indices and m^n components and obeys certain transformation rules.
or:
a tensor is a geometric object (vector, 3D reference frame) which is expressed and transformed according to a coordinate system defined by the user.

# IV- Alternate definitions

Tensor = a collection of vectors and covectors combined together using the tensor product. (kind of abstract/ recursive definition since you need to know about covectors and tensor product)

Alternate interpretation: Tensors as partial derivaties and gradients that transform with the Jacobian Matrix

# References

Good starter explanation

Others: 