00:00Hi guys, okay, so this
00:02is my first lesson on
00:04face portraits. I'm gonna do
00:05quite a few lessons on
00:06face portraits just because there's
00:07quite a lot to get
00:08through. This lesson I'm just
00:11gonna introduce them. Gonna explain
00:13what they are with the
00:15use of an example. So
00:16hopefully we're gonna just get
00:17the bigger picture here, what
00:18we're trying to do and
00:19what's the point of all
00:20this. And you're gonna see
00:23how it's related to these
00:24couple differential equations that we've
00:26been doing.
00:28already leading up to this.
00:29So firstly, what is a
00:31phase portrait? A phase portrait
00:33is a geometric representation of
00:34the trajectories of a dynamical
00:36system in the phase plane,
00:38right? There's probably a lot
00:39of words there that don't
00:42make any sense, but let
00:44me bring up this example
00:45and it will help explain
00:47everything. Okay, now do we
00:52remember the predator prey model
00:55that we
00:56I was talking about in
00:58the previous lesson. So essentially
01:01we have the x axis
01:04is prey and the y
01:06axis is the predators. In
01:08this case it might be,
01:10well I think it was
01:13foxes and rabbits in our
01:15previous lesson. So that's stick
01:16with foxes and rabbits. So
01:18essentially as the
01:24So this here is a
01:26trajectory. In fact, all these
01:27little arrows are trajectories. What
01:31they're telling us is, well,
01:33the arrow is pointing in
01:36the direction. The trajectory is
01:39going to go. Once we
01:41start the time, because remember
01:43time is another variable within
01:45this whole system. So once
01:48I'm going to start this
01:50animation in a second, you'll
01:52see
01:52that the arrows point which
01:54direction the trajectory is going
01:56to go. And it's basically
01:57going to go around here
01:59and come back to here.
02:00And the reason is if
02:01we remember our predator prey
02:03model, we start here. Let's
02:06say we start with 80
02:08rabbits and I don't know,
02:1030 foxes. What happens is
02:13the rabbits are fine. There's
02:18still multiple
02:20at this moment and they're
02:22increasing. So the number of
02:25rabbits is increasing increasing increasing
02:27because as we move along
02:28the trajectory the x is
02:29getting bigger. So the x
02:31is getting bigger bigger bigger.
02:32But the y is also
02:33getting bigger. So at this
02:36point here, all these points,
02:40the number of rabbits is
02:41getting bigger and the number
02:43of foxes is getting bigger.
02:46In terms of our
02:48couple differential equations are dx
02:51dt is positive and our
02:53dy dt is also positive.
02:57So they're both increasing increasing
02:59increasing increasing increasing increasing and
03:00then we get to this
03:01point here and what happens
03:02is there's about there's a
03:06lot of rabbits but there's
03:07so many foxes now that
03:09the foxes are starting to
03:10eat the rabbits and there's
03:13too many foxes they're eating
03:15the rabbits so the population
03:16the rabbits starts to slow
03:18down and the fox is
03:19starting to eat all the
03:20rabbits and they're eating them
03:21and eating them and eating
03:22them and we get to
03:23this point and then the
03:23fox is realized, oh, we've
03:26eaten all the rabbits are
03:28many of the rabbits. Well,
03:30we've eaten 100 rabbits from
03:31this point to this point,
03:33140 to 40. They've eaten
03:34the rabbits. So what happens
03:36is the population of the
03:38foxes just starts to decline
03:41and it goes down downtown.
03:44And the number of foxes
03:48start dying because there's nothing
03:49to eat. And then we
03:51get back to here, which
03:52is our initial condition, our
03:5480 foxes and our 80
03:58rabbits and our 30 foxes.
03:59And the whole thing starts
04:00again. And let me show
04:02you this animator just to
04:03show you what happens. So
04:08there you go. We're going
04:11to go back.
04:12to here. Okay, so we
04:17start with this many rabbits.
04:19They both increase and then
04:21we get to this point
04:22and then the rabbits just
04:23start to pull all the
04:24way down like this and
04:25then back all the way
04:26up to where they were.
04:29Similar, similarly with the predators
04:30and eat the rabbits. Rabbits
04:34start to die. Predators start
04:35to die and we go
04:36back to here and we
04:37go around and around like
04:38this. Let me just keep
04:39going.
04:40Now, if we change the
04:42initial condition, it changes the
04:47trajectory completely. Well, not completely,
04:50but it is changing, depending
04:51on where the initial condition
04:53starts. If we start with
04:54our 80, our 80 rabbits
04:57are our 30 foxes, this
04:59is going to happen. But
04:59if we change it to,
05:01I don't know, 80 foxes
05:04and... There's very 80 rabbits
05:07and 60 foxes.
05:08This is how it looks
05:11So the trajectory is actually
05:13a lot well, that's a
05:15lot smaller But it does
05:19come it does come back
05:21to the initial condition now
05:22what happens is if we
05:24if we put this initial
05:26condition in here Right in
05:30Here, let's imagine these are
05:32straight lines what happens is
05:34and let's say this is
05:35fifth I think it's 50
05:3650, pray, and 80 predators.
05:41What actually happens is this
05:43is called an equilibrium point
05:45where we're going to study
05:46this in more detail in
05:48the following lessons. But essentially,
05:51at that point, if you
05:53subdue in 50 and 80,
05:54if you subdue in 50
05:55for x and 80 for
05:58y, your DXDT would actually
06:01be zero. And if you
06:02subdue in 50 for x
06:04and
06:0480 for y in here,
06:06your dy dt would be
06:070. So the rate of
06:08change of x with respect
06:10to time and the rate
06:11of change of y with
06:12respect to time is actually
06:130, which means they're not
06:16changing. It's basically the perfect
06:19balance of foxes and rabbits
06:23in this particular situation. I'm
06:26not quite sure why it's
06:27so high for this seems
06:29like a lot of foxes,
06:30but whatever, according to this
06:32model,
06:33This is the point where
06:36it's called an equilibrium point.
06:39It is a well, we
06:41say the system. This is
06:43an equilibrium point. We say
06:44the system is stable and
06:47it just continues on like
06:50this. We just leave time.
06:52Go forever. There'll just always
06:54be 50 prey and this
06:57is supposed to be a
06:57straight line, by the way
06:58guys. I'm just perfectly in
07:00the right point.
07:01but whatever. Just pretend this
07:02is a straight line. You'll
07:04always have 50, uh, 50
07:08pray, 50 rabbits and 80
07:10foxes. So that's essentially is
07:13this is a phase portrait.
07:16It shows us all the
07:18trajectories of the dynamical system.
07:20This is a dynamical system.
07:21It's a system where the
07:23state changes over time. As
07:25you press play on the
07:26time when it changes. And
07:28so this
07:29the dynamical system, we go
07:30back to our definition. Well,
07:34this is the phase plane,
07:37our x, y plane, this
07:38is called the phase plane.
07:42And the phase portrait is,
07:44as I said, the geometric
07:45representation of the trajectory. So
07:47it's just the phase portrait
07:48is actually these arrows. Okay.
07:56Now the way we're
07:57going to actually sketch face
07:59portraits because we do need
08:00to be able to sketch
08:01face portraits is like this.
08:02We're actually going to sketch
08:04not every because there's actually
08:06an infinite number of trajectories,
08:08but we're going to sketch
08:09a number of trajectories to
08:13basically show that you understand
08:15what's actually happening in the
08:18face in the face plane.
08:20Okay, nearly done. So I'm
08:22just going to do this
08:23example here. Guys, I'm actually
08:24going to do this.
08:25example, I think is this
08:27one in a following video,
08:29but I just want to
08:30show you the kind of
08:33questions we're going to be
08:34dealing with. So it's related
08:36to these coupled differential equations,
08:38but they are going to
08:40be linear equations. So it's
08:42always of the form dx
08:44dt equals ax plus bi.
08:46So it's always ax plus
08:51bi.
08:53And so that's like our
08:55x, x with a dot,
08:58x dot, that's dx dt.
09:00And our y dot is
09:01always um, cx plus dy.
09:06So some kof abc and
09:07d are just coefficients here.
09:10Um, but it's always of
09:11this form. You're not going
09:12to get any squared or
09:14x, y or anything like
09:15that. Um, so that's our
09:19dx dt. That's our dy
09:20dt in this particular
09:21example. Now you can see
09:24that the equilibrium point for
09:27any of these, well for
09:30these couple differential equations of
09:32this type the equilibrium point
09:35where is dx dt and
09:37dy dt both going to
09:38be equal to zero, both
09:39going to equal to zero,
09:40well it's at zero zero
09:42at the origin. So the
09:44origin is going to be
09:45the equilibrium point when we're
09:49sketching
09:49these face portraits. A nice
09:52little thing we can do
09:53here and again I'm going
09:54to go into this in
09:55far more detail is we
09:56can write this in this
09:59form and then by finding
10:05the i in values and
10:07the eigenvectors. So these are
10:09the purple line here and
10:10the green line. These are
10:11actually the eigenvectors of, well,
10:14in the direction of the
10:15eigenvectors of this
10:17of this matrix. This matrix
10:201, 1, 4, 1 obviously
10:22comes from this 1, 1,
10:254, 1. And the eigenvalues
10:29will tell us a bit
10:30more about what's going on
10:31as I say. I'm going
10:32to go into a lot
10:33more detail when I explain
10:37that in the following videos.
10:39Okay, so hopefully that makes
10:41somebody sense guys. It's a
10:45topic
10:45that takes a while to
10:47get your head around but
10:49once you do it's actually
10:52it's actually not that bad
10:53but there's plenty just plenty
10:58to learn by the way
10:59this is the same this
11:03is just another way of
11:04writing this equation here this
11:08is a deliberately bolded x
11:10dot so this is actually
11:12a vector and it is
11:13supposed to be x
11:13it is defined as x
11:16dot y dot and then
11:17this x is a vector
11:19which is actually xy. So
11:20if you see this, if
11:21you see me using this,
11:22or if you see it
11:23in, and I've seen it
11:25in textbooks in various places,
11:28you might even see it
11:28on an example. That's what
11:29it means. It's basically this
11:31book. I'll be working more
11:33with this form. Okay, done
11:39guys. I'll see you in
11:41the next video.
11:41actually solve this in the
11:43next video and I'll show
11:44you how I drew that
11:45face portrait.