Ladies and gentlemen, what you see is the future of education. I am very excited to witness this.

I really appreciate your efforts in making all this knowledge available for free, you are doing humanity a great service.

Merci Mr Brunton, c’est toujours un plaisir de vous écouter ! Pour moi le matin en prenant mon petit déjeuner. Impatient d’écouter la suite.

Thank you, prof. Brunton. Your style of explaining, at least for me, it's very effective. Please keep up this great work!

beautifully explained !! thank you! i passed all my first and second year algebra courses with minimum effort during the pandemic so learning this for my scientific computing course has been hell, but you make it so simple i really get it now !!! you're a great teacher !!

What a fantastic presentation. You are a great teacher!

Extraordinary lecture. Thank you, Steve.

thanks for explaining this in such an enthusiastic way. u saved my semester

wow! i'm excited to see how different this will be for stochastic systems.

An amazing explanation! You nailed it. Thanks

amazing way of explaining rk4!!

Thank you very much for this great stuff. You are amazing...🌺🌺🌺.

Great explanation!

didn't understand much about it, but i like the video, thanks for sharing and making it

Your explanation of why the RK four method works is wonderful. It would be equally wonderful, as an example of the history of mathematics, if you could give some discussion on how the formula was derived.

So grateful for this lecture. It helped make sense of my notes. Thank you very much! :D

@Eigensteve

8 ай бұрын

You're very welcome! Glad it was helpful.

Great lecture series! Hopefully you'll get into symplectic integration-schemes too - for dynamical systems with conservation of some properties like energy and momentum.

pretty cool to be a long descendant of the author for this numerical integrator (Carl Runge)

@Eigensteve

2 ай бұрын

That's super cool!!

I wonder if there was a hidden joke in the RK2 explanation, or; if it were a complete random occurrence 🤔. These lectures are amazing. They relieve so much stress caused by the heavy reading of the text material. After watching these the reading becomes way easier.

Does anyone know where to get a transparent board which is used by Mr Brunton?

So I dont think it will be better than rk but I am curious about the performance benefits of using previous states to estimate curvature and higher order properties, keeping the call to dynamics function only once or somewhere in between. IE x(n+1) is not just dependant of x(n) but upto x(n-m). Given a pair of position and velocity(m=1) a cubic polynomial can be fitted and taken a time step over. Is there any good comparison for such a techniques?

Shouldn't the f_i be first order derivatives or tangents? They become vectors only after you multiply them with delta_t or not?

(consider adjusting the compressor in the audio, i _think_ you may need to decrease the attack (?) time?)

I almost don't want to point this out as this lecture is so beautiful: I think at t=12:01 the blue point @Eigensteve labelled as f1 should instead be labelled as [x_k + /delta(t)*f_1]. In my mind this would then mean that f_2 is calculated halfway on this vector at coordinates [x_k + /delta(t)/2*f_1]. So we are evaluating the vector field in the direction of f1 at a "distance" [/delta(t)/2*f1] away from x_k (i.e. it's in the direction of f1 but farther out). On another note the farther out than f1 implies /delta(t)/2 > 1. This is probably not often the case. I think we often choose /delta(t) around 0.01. This would mean the point where we calculate f_2 is actually closer to x_k than the magnitude of f_1. All these nitty-gritty details "erode" the beauty of the lecture and blurry the intuition that Steven is building up in our mind - an intuition that is more important than the details. Though in practice the details might become important. Hopefully I am not mistaken.

wonderful teaching, how are you writing on that board

Thou has done unto me that which is good.

would be interesting to have an overview on how to handle RK4 when f(x,t) is not analytical but is only known as a bunch of discrete values (sampled values or data-driven). Then, how to get \Delta{t}/2 values+

@dexdrurglum

Жыл бұрын

In my experience when I have only a dataset of discrete values, I just interpolate to get values that don't already exist in the dataset

@fabiofarina9579

Жыл бұрын

@@dexdrurglum yeah, I usally do quadratic or spline interpolation but I'd really enjoy a video from Steve on this topic

@dexdrurglum

Жыл бұрын

@@fabiofarina9579 I agree! Steve is the king 👑

@alexistremblay1076

Жыл бұрын

How about recurrent neural networks? Not entirely sure how it would handle future time steps.

Thanks for this great explanation! Just the bracelet I find distracting

f1 and f2 are not _directions_ they are complete values, they do not have length of 1 necessarily?

very helpfull

@Eigensteve

6 ай бұрын

Glad you think so!

does the runge-kutta 4 method need a root solver for nonlinear ode? if not, why? And instead why, for example, does the forward and backward euler method need a root solver (example: newton's method) for nonlinear ode?

@chensong254

Жыл бұрын

I don't think we need a root solver for RK4 or FE. We are just evaluating the derivative function f at certain points, and derivative f can be either linear or nonlinear. As long as the derivative is represented in an explicit form, we don't need a root solver. For BE though, I think we need a root solver if f is nonlinear.

@sim1_7

Жыл бұрын

@@chensong254 yes, i think you are right. My knowledge at this point Is that explicit methods are Linear in the variable a time k+1 so there Is no Need for root solver algorithms. Implicit methods, instead, are nonlinear with respect to the variable at time k. I tried explicit RK-4 for some nonlinear odes and it works pretty well. Forward Euler, instead, works bad for these nonlinear odes. It works good if non-linearities are weak but as soon as they become more complex FE fails

So, could I keep taking more halves for RK5, RK6, RK7, RK8 ... "RKn" in order to make it more accurate? (Of course, considering only accuracy since it will be to expensive to compute them, probably)

@chensong254

Жыл бұрын

I believe after a certain point, the benefit of using a higher order scheme becomes negligible compared to the floating point round-off error.

@fabiobiffcg4980

Жыл бұрын

@@chensong254 yeah, after some thinking, that's what I thought

Amazing！ How can he write in the back of the glass (which is like a mirror effort) but still keep the numbers normal?

@theintjengineer

Жыл бұрын

He writes just like you and I usually would. It's the board's mirroring property that reverses it and makes it look like he's writing reverse/backward/whatever.

@henrydevelopment

11 ай бұрын

The AI of a thermos flask. When you put hot water in it, it keeps it hot. When you put cold water in it, it keeps it cold. How does it know?

@pietheijn-vo1gt

9 ай бұрын

Very simple. Film normally, mirror the video in your recording software...

Can you also keep a live doubt solving session on Numerical Analysis?

@hoseinzahedifar1562

Жыл бұрын

It is a good idea...👍

@pietheijn-vo1gt

9 ай бұрын

why do all indians call 'problems' as 'doubts'?

a real example instead of so many functions, integral, delta etc

Are you writing backwards!? A short video showing how you use a glass window to make these videos would be really cool!

interesting that glass board? Is he writing left-to-right?

@ac2italy

Жыл бұрын

kzread.info/dash/bejne/l4qDsqaKZa6_o9I.html&ab_channel=ScopeTraining

@pietheijn-vo1gt

9 ай бұрын

Ofcourse. Just mirror the video in your recording software