1
00:00:00,540 --> 00:00:01,859
So welcome back to the cause.

2
00:00:01,920 --> 00:00:08,010
Let's get started with this new section about derivatives and actually in the previous section, which

3
00:00:08,010 --> 00:00:11,520
was about interpellation and about serious expansions.

4
00:00:11,880 --> 00:00:18,390
We have already encountered derivatives before the Taylor expansion, we needed first and also higher

5
00:00:18,390 --> 00:00:21,600
order derivatives for approximating a function.

6
00:00:22,320 --> 00:00:26,760
And back then, we have used a very simple version of calculating a derivative.

7
00:00:27,060 --> 00:00:30,960
And I told you that we will later on explore this in more detail.

8
00:00:31,620 --> 00:00:32,640
So now's the time.

9
00:00:33,150 --> 00:00:39,240
I will show you different ways on how to implement a derivative in Python or in numerics generally.

10
00:00:39,900 --> 00:00:43,560
So there are the methods called forward and backward differences.

11
00:00:43,920 --> 00:00:45,510
Central differences methods.

12
00:00:45,900 --> 00:00:49,200
And then we also discussed the so-called Richardson method.

13
00:00:49,860 --> 00:00:56,430
And the idea of these methods is that when you just have some data points, then you can calculate the

14
00:00:56,430 --> 00:01:02,670
derivative that describes or the derivative of the function that describes two data points with a higher

15
00:01:02,670 --> 00:01:06,180
accuracy when you use these methods.

16
00:01:06,210 --> 00:01:09,270
For example, the central differences method or the Richardson method.

17
00:01:10,050 --> 00:01:11,850
So maybe this sounds confusing to you.

18
00:01:11,850 --> 00:01:15,120
How it can you get a better result when you just use the same data?

19
00:01:15,570 --> 00:01:18,510
But it really works, and I want to show you how it works.

20
00:01:18,960 --> 00:01:24,720
And even you will explore this yourself because as an exercise in the middle of this section, I will

21
00:01:24,720 --> 00:01:31,260
give you a dataset where where we will investigate the motion of a car, for example, I give you the

22
00:01:31,260 --> 00:01:37,080
position of the car at several points of times, and then I ask you to calculate the velocity and the

23
00:01:37,080 --> 00:01:42,000
acceleration using the different methods that we have discussed before.

24
00:01:42,720 --> 00:01:48,750
And then you will see that some of these methods are way more accurate than other methods than in the

25
00:01:48,750 --> 00:01:50,790
end of the course or of the section.

26
00:01:51,090 --> 00:01:53,850
We will discuss multi-dimensional derivatives.

27
00:01:54,450 --> 00:01:59,160
So this means we will discuss the concept of gradients, curls and divergences.

28
00:01:59,940 --> 00:02:05,520
And these are extremely relevant in physics, for example, in the electrodynamics, when we describe

29
00:02:05,520 --> 00:02:11,940
the Maxwell equations, for example, and we will actually discuss a physical problem in the next section

30
00:02:12,300 --> 00:02:19,830
where we will combine integrals and these multidimensional derivatives to calculate the magnetic fields

31
00:02:20,070 --> 00:02:21,240
of a charged wire.

