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Current time:0:00Total duration:7:03

- [Voiceover] Hello hello again. So, in the last video, I started talking about how you interpret the partial derivative of a
parametric surface function, of a function that has
a two variable input, and a three variable vector-valued output. And we typically visualize
those as a surface in three dimensional space, and the whole process,
I was saying, you think about how a portion of the t-s plane moves to that corresponding output. And again, I'm kind of
cheating with this animation, where, really, this isn't
the t-s plane, right? This is on the x-y plane. t-s plane should just be some
separate space over here, and we're imagining
moving that separate space over into three dimensions. But that's harder to animate,
so I'm just not gonna do it, and I'm gonna instead keep
things inside the x-y plane here. We're thinking about the squares being t and s ranging each from 0 to 3. And, what I said, for partial
derivative with respect to t, is you imagine the line that represents movement
in the t direction. You see how that line gets mapped as all of the points move to
their corresponding output. And the partial derivative vector gives you a certain tangent vector to the curve representing that line, which corresponds to
movement in the t direction. And the longer that is,
the faster the movement, The more sensitive it is to
nudges in the t direction. In the s direction,
let's say we were to take the partial derivative with respect to s. So I'll kind of clear this up here. Also clear up this guy. And if you said instead, "what if we were doing it
with respect to s," right? Partial derivative of v, the vector-valued function,
with respect to s. Well, you do something very similar. You would say "OK, What is
the line that corresponds "to movement in the s direction?" And the way I've drawn it, it's always going to be perpendicular, because we're in the t-s plane, the t axis is perpendicular
to the s plane. And in this case, this line
represents t = 1, right? You're saying t constantly equals 1, but you're letting s vary. And if you see how that line maps, as you move everything
from the input space over to the corresponding
points in the output space, that line tells you what happens as you're varying the
s value of the input. It kind of starts curving this way, and then it curves very much up and kind of goes off into the distance there. And again, the grid
lines here really help, because every time that you
see the grid lines intersect, one of the lines represents
movement in the t direction, and the other represents
movement in the s direction. And for partial derivatives,
we think very similarly. You think of that partial
s as representing- (voiced zooming sounds) Zoom on back here. That partial s you
think of as representing a tiny movement in the s direction, just a little smidge and nudge, somehow nudging that guy along. And then the corresponding nudge you look for in the output space, you say okay, if we nudge
the input that much, and we go over to the output, and... and maybe that tiny nudge corresponded with one
that's three times bigger. I don't know, but it looked
like it stretched things out. So that tiny nudge might
turn into something that's still quite small, but maybe three times bigger. But it's a vector. What you do is, you think of that vector as being your partial v, and you scale it by whatever the size of that partial s was, right? So the result that you get is a tangent vector that's
not puny, not a tiny nudge, but is actually a sizable tangent vector. And it's going to correspond to the rate at which changes, not just tiny changes, but the rate at which changes in s cause movement in the output space. So, let's actually
compute it for this case, just get some good
practice computing things. And if we look up here, the t value which used to be considered a variable when we were doing it with respect to t. But now that t value
looks like a constant, so its derivative is zero. Then -s², with respect to s, has a derivative of -2s. st: s looks like a variable,
t looks like a constant, the derivative is just that constant, t. Down here, ts²: t looks like a constant, s looks like a variable, so... 2 times t times s. And then over here, we're subtracting off, s is the variable, t²
looks like a constant, so that constant. And let's say we plug in the value (1,1). This red dot corresponds to (1,1), so what we would get here, s is equal to 1, so that's -2, t is equal to 1, so that's 1, then 2 times 1 times 1, I'll write it. 2*1*1 minus 1² is gonna correspond to 1, that's 2-1. So what we would expect
for the tangent vector, the partial derivative vector, is the x-component should be negative, and then the y and z-components
should each be positive. And if we go over, and we take a look at what the movement along
the curve actually is, that lines up, right? Because, as you kind of
zip along this curve, you're moving to the
left, so the x-component of the partial derivative
should be negative. But you're moving upwards
as far as y is concerned, and you can also kinda see
that the leftward movement is kind of twice as fast
as the upward motion. The slope favors the x direction. And then as far as the z
component is concerned, you are, in fact, moving up. And maybe you could say, "Well, how do you know
which way you're moving, "are you moving that way, or is everything "switched the other way around?" And the benefit of animation here is, we can say, "Ah, as s is ranging from zero "up to three, this is the
increasing direction." And you just keep your eye
on what that direction is as we move things about. And that increasing direction
does kind of correspond with moving along the curve this way. So, you get a tangent
vector in the other way. And one kinda nice thing
about this, then, is the two different partial
derivative vectors that we found Each one of them, you could say, is a tangent vector to the surface, right? So the one that was a partial derivative with respect to t, over here, kinda goes in one direction, and the other one kinda
gives you a different notion of what a tangent vector
on the surface could be. And you could have a notion
of directional derivative too, that kind of combines
these in various ways, and that will get you
all the different ways that you can have a vector
tangent to the surface. And later on, I'll talk about
things like tangent planes, if you want to express
what a tangent plane is. And you kind of think
of that as being defined in terms of two different vectors. But for now, that's really
all you need to know about partial derivatives
of parametric surfaces. And the next couple
videos, I'll talk about what partial derivatives
of vector-valued functions can mean in other contexts, because it's not always
a parametric surface, and maybe you're not always thinking about a curve that could be moved along, but you still want to think,
"How does this input nudge "correspond to an output nudge, "and what's the ratio between them." So with that, I'll see you next video.