Intuition The ONE idea behind all of tensor analysis
A physical thing — the temperature in a room, the push on a wall, the velocity of a fly — exists
before you draw any axes. A tensor is the bookkeeping object whose numbers rearrange in a
fixed, predictable way when you swap your axes, precisely so that the thing itself never changes.
Everything below is the vocabulary you need to make that sentence exact.
This page assumes you have seen nothing . We meet every letter, arrow, subscript and superscript
the parent note uses, in an order where each
one is built only from things already explained.
Definition What a coordinate is
A coordinate is just a number that tells you "how far along" a chosen direction you are. To pin
a point in a flat plane you need two numbers; in space, three. We call the whole list of them
x 1 , x 2 , …
Notice the number sits up high : x 1 , x 2 . Read that as "x-one, x-two" — it is not "x
squared". This raised label is an index , our first piece of notation.
x 2 means x-squared."
Why it feels right: that is what a raised 2 means in ordinary algebra. Fix: in tensor
notation a raised number after a coordinate is a name tag (the 2nd coordinate), not a power.
When we genuinely want a square we write ( x 2 ) 2 . Context tells them apart; we will always warn you.
An index is a little letter or number attached to a symbol that says "which slot am I talking
about". V i with i standing for "any of 1.. n " is shorthand for the whole list
V 1 , V 2 , … , V n .
n = the number of dimensions (how many coordinates), so i runs 1 to n .
Upper index like V i and lower index like W i will turn out to mean different kinds
of thing (Section 6). For now: it is a slot number, and its height is a flag we will learn to read.
Intuition Why bother with indices at all?
Because a law like "force equals mass times acceleration" is really n little equations at once
(one per direction). Writing F i = m a i says all n of them in five symbols. The index is the
compression.
The count of numbers is n rank : a scalar has n 0 = 1 , a vector n 1 = n , a rank-2 tensor
n 2 . This is the whole "hierarchy" the parent note draws.
Definition The three characters
Scalar ϕ : one number that is the same whichever axes you use. Picture: a temperature
reading on a thermometer — rotating your body doesn't change it.
Vector V i : an arrow with a length and a direction. Picture: an arrow drawn on the plane.
Its components V 1 , V 2 are the shadows it casts on the two axes.
Rank-2 tensor T ij : a machine that takes in directions and returns directions/numbers.
Picture: a table ( T 11 T 21 T 12 T 22 ) with a rule
attached for how it changes when axes rotate.
The word component just means "one of the shadow-numbers". The arrow is the real thing; the
components are what a particular choice of axes reports.
Intuition Why change coordinates at all?
The whole subject only becomes interesting because the same arrow gives different component
numbers on different grids. Tensors are the objects that survive this. So we must first be crystal
clear about what "change of axes" does to numbers. (Deeper: Linear algebra — change of basis & similarity transforms .)
Look at the figure. One fixed orange arrow. On the black axes its shadows are ( V 1 , V 2 ) ; on the
teal rotated axes the shadows are ( V ~ 1 , V ~ 2 ) — different numbers, same arrow . The
tilde ~ over a symbol always means "measured in the new coordinates".
To say precisely how new numbers relate to old, we need the tool that measures "how much does one
quantity change when another nudges". That tool is the derivative .
Definition Partial derivative
∂ x k ∂ x ~ i
∂ x k ∂ x ~ i reads: "if I nudge the old coordinate x k by a
whisker, how much does the new coordinate x ~ i move?" The curly ∂ ("partial")
means we wiggle one variable and freeze all the others.
this tool and not just multiplication?
If the coordinate change were a plain stretch (x ~ = 2 x ) a single multiplier would do. But
changes can be different at every point (think polar coordinates, where a step in θ means
a big move far from the origin, a tiny move near it). Only the derivative captures a rate that
varies point-to-point. That is exactly why calculus enters tensor analysis.
Definition The Jacobian matrix
J i k
Collect all those partial derivatives into a grid:
J i k = ∂ x k ∂ x ~ i .
Row i , column k tells you how new-coordinate-i responds to old-coordinate-k . Its inverse
( J − 1 ) k j = ∂ x ~ j ∂ x k runs the translation the other way.
This is the central engine of the parent note: every transformation law is "attach one Jacobian
factor per index". See Jacobian and the multivariable chain rule for the machine itself.
Now the height of the index earns its keep.
Definition Contravariant (upper) — behaves like a
displacement
A contravariant vector V i transforms with the forward Jacobian:
V ~ i = ∂ x k ∂ x ~ i V k .
Picture: a small step d x k across the grid. If you switch to a grid with a longer ruler, the
numbers get smaller (fewer ruler-lengths fit the same step) — it changes contra (against) the
ruler.
Definition Covariant (lower) — behaves like a
gradient
A covariant vector W i transforms with the inverse Jacobian:
W ~ i = ∂ x ~ i ∂ x k W k .
Picture: the steepness of a hill, ∂ ϕ / ∂ x k . A longer ruler makes the "rise per
ruler-length" bigger — it changes co (with) the ruler.
Mnemonic Which way is which
CO variant goes the same way as the basis (both "co-operate"); CONTRA variant fights it.
Upper index = the displacement-like one; lower index = the gradient-like one.
both
Because when you multiply one of each and sum (next section), the forward Jacobian and its inverse
cancel perfectly, leaving a number that no coordinate change can touch — a true scalar. Two kinds
exist so that they can annihilate each other's transformation and produce invariants.
The distinction between V i and V i only becomes numerical in bent or stretched coordinates,
where you convert between them using the metric — see Metric tensor and Riemannian geometry .
Definition Einstein summation
Rule: whenever an index appears once up and once down in the same term, you silently add
over all its values. So
W i V i ≡ ∑ i = 1 n W i V i .
The repeated index is called a dummy index; it is summed away and vanishes from the answer.
∑ ?
Tensor formulas are drowning in sums; writing ∑ everywhere buries the structure. Einstein
noticed the sum always pairs one upper with one lower index, so the ∑ carries no information
— drop it. What is left, W i V i , is exactly the cancellation of Section 6: a genuine scalar.
Full details: Einstein summation convention .
Definition Kronecker delta
δ j i
δ j i = { 1 0 i = j i = j
Picture: a grid with 1 s down the diagonal and 0 s everywhere else — the identity matrix, written
with indices. Its job: when you contract it with a vector it just relabels the index, δ j i V j = V i .
It shows up because the Jacobian times its inverse collapses to it:
∂ x k ∂ x ~ i ∂ x ~ j ∂ x k = δ j i — the
statement "go to new coordinates and back, and nothing happened".
Definition Symmetric / antisymmetric
A rank-2 tensor is symmetric if S ij = S j i (mirror across the diagonal leaves it unchanged),
antisymmetric if A ij = − A j i (mirror flips the sign, forcing zeros on the diagonal).
Picture two grids: one that reads the same reflected across the main diagonal, one that turns into its
own negative. Because the transformation law treats both indices identically, this mirror-property is
the same in every frame — a coordinate-free fact, which is why physicists trust it (stress is
symmetric in any axes — Stress and strain tensors (continuum mechanics) ).
You now hold every symbol the parent note uses. Two live beyond this foundation and get their own
pages later — flagged here only so you know they exist:
Scalar vector rank-2 tensor
Change of basis new numbers
Contravariant upper index
TENSOR TRANSFORMATION LAW
Test yourself — each should feel obvious before you tackle the parent note.
What does the raised label in x 2 (as a coordinate) mean? The second coordinate — a name tag, not "x squared".
An index i in V i stands for what? Any slot number from 1 to n ; V i is shorthand for the whole list of components.
Rank of a tensor equals what, and how many components does it have? The number of indices; it has n rank components in n dimensions.
Difference between a scalar, a vector, and a rank-2 tensor? 0 , 1 , and 2 indices — one number, an arrow, and a grid-with-a-transformation-rule.
What does the tilde in x ~ i signal? The quantity measured in the new coordinate system.
In words, what is ∂ x k ∂ x ~ i ? The rate at which new-coordinate-i changes when old-coordinate-k is nudged.
Why does the derivative (not plain multiplication) drive coordinate changes? Because the change rate can differ from point to point; only a derivative captures a locally-varying rate.
Which vector uses the forward Jacobian, and what is it called? The contravariant (upper-index) vector, V ~ i = ∂ x k ∂ x ~ i V k .
Which uses the inverse Jacobian? The covariant (lower-index) vector, W ~ i = ∂ x ~ i ∂ x k W k .
Why must both kinds exist? So an upper-lower pair cancels its Jacobians and yields a coordinate-independent scalar.
State the Einstein summation rule. A repeated index appearing once up and once down is summed over 1.. n ; the ∑ is dropped.
What is δ j i and what does ∂ x k ∂ x ~ i ∂ x ~ j ∂ x k equal? The identity/Kronecker delta; the product equals δ j i (go and come back, nothing changes).
Why is symmetry (S ij = S j i ) a "real" property? The transformation law treats both indices alike, so symmetry holds in every coordinate frame.