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## Multivariable calculus

### Course: Multivariable calculus > Unit 2

Lesson 10: Curl- 2d curl intuition
- Visual curl
- 2d curl formula
- 2d curl example
- Finding curl in 2D
- 2d curl nuance
- Describing rotation in 3d with a vector
- 3d curl intuition, part 1
- 3d curl intuition, part 2
- 3d curl formula, part 1
- 3d curl formula, part 2
- 3d curl computation example
- Finding curl in 3D
- Symbols practice: The gradient

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# 3d curl formula, part 1

How to compute a three-dimensional curl, imagined as a cross product of sorts. Created by Grant Sanderson.

## Want to join the conversation?

- In the video about the cross product, Sal said the cross products are only defined in 3-d, while here you said that it could be defined in 2-d.

So, did Sal originally meant that cross products are defined in 3-d`or less`

?(9 votes)- I think he meant this:

If you are working in two dimensions, let's say in the x-y plane, the cross product will be a vector in the z direction. If you are in the x-z plane, the cross product will be a vector in the y direction. If you are in the y-z plane, the cross product will be a vector in the x direction. Hence the reason why Sal said that the cross products are only defined in 3-d.

Maybe he meant something else or just made a mistake but that's my understanding of it. :)(8 votes)

- Where would one find this cross product video Grant mentioned? I've done the matrices playlist and only the dot product was brought up.(4 votes)
- I think there are introductory videos in Precalculus, and there are more in Linear Algebra / Vectors and Spaces.(3 votes)

- I'm really confused with the use of the cross product to represent curl; we take the product of each partial derivative with its corresponding components and subtract them from each other, which gives us the 2-D formula for curl, but previously we have been taking the partial of P with respect to y for example. I thought that taking a partial derivative is an operation rather than a multiplication?(3 votes)
- So using the cross product, is curl generalizable to more than 3 dimensions?(2 votes)
- is the value of vector,V same in the whole 3d space? for example if we take v vector as velocity of air, sometimes it may flow at different speed at some locations. So how come a sine function represents the velocity of whole air?(2 votes)
- Why does computation go as nabla X vector, why not the other way around- vector X nabla?(1 vote)
- For cross-products, order matters, so a X b is not the same as b X a (a X b = -(b X a)). The nabla X vector notation isn't really a calculation, it's more of a way to remember the formula for curl (a "notational trick", as Grant says).

Hope this helps!(3 votes)

- Two questions:

1)are the "tricks"/methods Grant is using standard procedure among mathematicians?

2) how did anyone visualize/manipulate these concepts prior to having access to computer generated graphing programs?

It seems to me that this whole realm of math is pointing at least towards quantum theory --please correct if I'm mistaken -- and we know that those folks did not have computers. Any insight is appreciated.(1 vote)

## Video transcript

- [Voiceover] So I've
spent a couple videos laying down the foundation
for what three-dimensional curl is trying to represent, and here I'm going to go ahead and talk about how you actually compute it. Three-d curl is the kind
of thing that you take with regards to a
three-dimensional vector field. So something that takes in
a three-dimensional point as its input, and then
it's going to output a three-dimensional vector. It's common to write the component
functions as P, Q, and R. Each one of these is a
scale or valued function that takes in a three-dimensional point, and just outputs a number. So, it'll be that same three-d point with the coordinates x, y, and z. So when you have a three-dimensional
vector field like this, the image you might have in mind would be something like this, where every point in three-dimensional space
has a vector attached to it. And you know, when you
actually look at it, there's quite a lot going
on, but in principle, all that's really happening
is that each point in space is associated with a vector. The point in space is the input and the vector is the output. You're just gluing them together. Naturally, between the three
dimensions of the input and the three dimensions of the output, we have six dimensions going on, the picture that you're
looking at becomes quite messy. So, the question is, how do
you compute this curl value that I've been talking about. Curl of your vector value function. Just as a quick reminder,
what this is supposed to be, is you're going to have
some kind of fluid flow induced by this vector
field, where you're imagining air flowing along each vector. What you want is a function that tells you at any given point, what is the rotation induced by that fluid
flow around that point. Because rotation is described with a three-dimensional vector, you're expecting this to be vector-valued. It'll be something that
equals a vector output. If that doesn't make sense,
if that doesn't quite jive, maybe go check out the
video on how to represent three-dimensional rotation with a vector. So what you have here
is going to be something that takes as its input, x, y, and z. It takes a three-dimensional point, and what it outputs is a
vector describing rotation, and there's actually another notation that's quite, quite helpful
when it comes to computing this. You take nabla, that
upside-down triangle we used in divergence and
gradient, and you imagine taking the cross-product
between that and your vector V. As a reminder, this nabla, you imagine it as if it's a vector containing partial differential operators. That's the kind of thing where,
when you say it out loud, it sounds kinda fancy, a vector full of partial differential operators, but all it really means is I'm just going to write
a bunch of symbols. This partial partial x
is something that wants to take in a multi-variable function, and tell you its partial derivative. Strictly speaking, this
doesn't really make sense, like, hey, how can a vector contain these partial differential operators? But as a series of symbolic movements, it's actually quite helpful, because when you're multiplying
these guys by a thing, it's not really multiplication. You're really going to be giving it some kind of multi-variable
function, like P, Q, or R, the component functions
of our vector field, and evaluating it. So just as a warm-up for how to do this, let's see what this looks like in the case of two dimensions, where
we already know the formula for two-dimensional curl. What that would look like,
is you have a smaller, more two-dimensional,
just partial partial x, partial partial y, del operator. You're going to take the
cross-product between that and a two-dimensional vector that's just the component
functions P and Q. In this case, P and Q would
be just functions of x and y. So I'm kind of overloading notation right, over here I have a
two-dimensional vector field that I'm saying, P and y
are scale or value functions with a two-dimensional
input, but over here I'm also using P and Q to represent ones with a three-dimensional input. So you should think of these as separate, but it's common to use the same names. This is going to illustrate the broader, more complicated point. When you compute something
like this, the cross-product, you typically think of it as taking these diagonal components
and multiplying them, so that would be your partial partial x, "multiplied" with Q, which
really means you're taking the partial derivative
of Q with respect to x. Then you subtract off this
diagonal component here, oh sorry, this should be a y. This should be partial partial y. Sorry about that. You need partial partial y of P, and that's what you're subtracting off. So partial partial y of P,
just the partial derivative of that P function with respect to y. Hopefully this is something you recognize. This is the two-dimensional curl. It's something we got an intuition for, I want it to be more than just a formula, but hopefully this is kind of reassuring that when you take that del
operator, that nabla symbol, and cross-product with the
vector valued function itself, it gives you a sense of curl. Now when we do this in the
three-dimensional case, we're going to take a
three-dimensional cross-product between this three-dimensional
vectorish thing and this three-dimensional function. If you're not terribly comfortable
with the cross-product, how to compute it or how to interpret it and things like that, now would probably be a good
time to go find the videos that Sal does on this and
build up that intuition for what a cross-product actually
is and how to compute it. Because at this point, I'm
going to assume that you know how to compute it
because we're doing it in kind of an absurd context of
partial differential operators and functions, so it's important
to have that foundation. The way you compute a thing like this, is you construct a determinant. I'm going to go down here. Determinant of a certain 3x3 matrix. The top row of that is
all of the unit vectors in various directions of
three-dimensional space. So these I, J, and K guys,
I represents the unit vector in the x direction, so that
would be I is equal to, x component is one but then
the other components are zero. Then similarly, J and K
represent the unit vectors in the y and z direction, and again, if that doesn't quite make sense, why I'm putting them up there
or what we're about to do, maybe check out that cross-product video. So we put those in the
top rows as vectors. This is the trick to
computing the cross-product, because again, what does
it mean to put a vector inside a matrix, but
it's a notational trick. Then we're going to take the first vector that we're doing the cross-product with, and put its components in the next row. What that would look like,
is the next row has a partial partial y, no sorry, God I keep messing up here, that's an x, you do whatever
the first component is first, and then the second component second, and the third component, the z, partial z. Don't know why I'm making
that little mistake. For the last row, you
put in the second vector, which is in this case, is vector
value function P, Q, and R. P, which is a multi-variable
function, Q, and R. First, it's worth stepping
back and looking at this. This is kind of an absurd thing. Usually when we talk about matrices and taking the determinant, all of the components are numbers because you're multiplying
numbers together. But here, we've got a
notational trick layered on top of a notational trick,
so that one of the rows is vectors, one of the rows is partial differential operators, and then the last one, each one of these is a
multi-variable function. So it seems like this absurd, convoluted, as far away from a matrix
full of numbers thing as you can get, but it's actually very helpful for computation. If you go through the process
of computing this determinant and saying, what could that mean, the thing that pops out
is going to be the formula for three-dimensional curl. At the risk of having a
video that runs too long, I'll call things and end here, but continue going through that
operation in the next video.