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Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!

Frames Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!

Description Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!

Comments Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!

I’m so happy I met this video, I’m currently exploring SINDy as a method of equation discovery and I am actually having trouble with the Python package pysindy brThere’s a consistent error of npmathsfactorial or listT, how do I get the correct or stable version of the package Comment from : @chidiokoene1418

ahhh! Just found that you are the author of SINDY! Comment from : @AinsleyElizabeth-gp5zt

Amazin brother! Now new Syndy hardware becomes demanding on the market Comment from : @myelinsheathxd

Steve, might a discrete dynamical system be an appropriate application for the SINDy technique or is SINDy more optimized for continuous systems? Comment from : @sposty1

Has anybody tried to reverse engineer Kepler's laws out of Tycho🎉 Brahe's data? Comment from : @musicarroll

Could you please link the papers that use SINDy technology to learn non-linear dynamics from noisy data or data having stochastic dynamics? Many thanks! Comment from : @youknowmyname12345

Your work is really exceptional and deserve a salutegreat work!! Comment from : @webtechbysuraj5942

I have a few questionsbrbrWhat is the use of Neural networks in this framework? I mean, if you define the polynomials in advance, why do we need NNs?brbrWhy cannot we find the parameters by using the standard regression techniques, like in established methods?br brHow is this different from standard well-established system identification methods?brbrHow does this compare with the methods in the literature? What are its advantages over the other methods developed so far in the literature? Comment from : @kesav1985

beautiful algorithm Comment from : @echonicr

How effective is SINDy to identify Saturation Nonlinearities? Comment from : @AdeyemiAlabi

Hi steve, thank you for such fantastic videos and give me purpose as a mechanical engineer on what to do in future Comment from : @backbench3rs659

How does this differ from AI feynman?? Comment from : @DistortedV12

Hello Steve, thank you for sharing this knowledge I have a question, have you ever seen this applied to system dynamics (in the field of what JayW Forrester proposed), like how from data we could learn the structure of the system (diagrams of flows and levels) or how we can exract the behaviour patterns of the system from data to find the archetypes(ie Limits to growth, tragedy of commons,Escalation, that are explained in some works of Donella Meadows) that system is experiencing Thank you! Comment from : @danielmuar3920

First principles----->Occam's Razor---->Sparse modelbrSo, God, human, and machines, they converge now? Comment from : @zhongzhongclock

Absolutely love yours and Nathan work Going thru your book on DDSE The animations are always great!! Thank you for sharing!! Comment from : @macmos1

Hmm, you take the humor out of Einstein’s quote We want things as simple as possible, but not the impossible! But I get it Too sparse does and it does not work Comment from : @vtrandal

steve, having come from a deep learning background, this topic is so refreshingly transparent and elegant thanks so much for the beautifully presented material I'm excited to start exploring SINDy for the problems im looking at Comment from : @duncanhay9779

Hi Professor Steve, LOVE YOUR VIDEOS Have you considered forming a discord group or something like that? There's quite a following on your channel, I am sure there are many people like me would like a discussion on control/ Sparsity alike topics Comment from : @abdjahdoiahdoai

Just noticed this video was uploaded a couple of weeks ago Look forward to the following videos Thanks, Prof Brunton Comment from : @ericcartman106

Sir can you tell me what is future of software engineers Comment from : @loki-oq1lj

Wow! Now that is a good use of machine learning Comment from : @michaelkree

i👏👏👏👏👏👏👏/i Comment from : @Nytrouse

Thanks a lot ! Comment from : @changhou6866

Amazing talk! Comment from : @alexwen950105

This is very beautiful and useful methodology! I think, it has potential applications to controlling undesirable or pathological chains of chemical reactions in a human body Just speculating, may be it can be applied to understand deeper cancer or prion diseases It would be also interesting to know if the method is resilient to noisy measurements Looking forward to your amazing lectures! Comment from : @eugenekoskin8292

Waw this is the ultimate science and technology can we reverse engineer and rediscover Schrodinger, Navier-Stocks and probably GR!!! Comment from : @riadhalrabeh3783

Amazing! Comment from : @vitorbortolin6810

Cant wait professor! Send them over, quick! :)) Comment from : @samirelzein1095

Very interesting Comment from : @Pedritox0953

i am really interested thanks for the videos,professor Comment from : @locutusdiborg88

I kind of miss the marker-on-glass videos Make more great lectures :) Comment from : @alegian7934

Can't wait for your next video Comment from : @GaryLee

Great video, Steve! Can't wait to see the rest of the series :) Comment from : @mattkafker8400

Another great video, thank you for the knowledge Comment from : @CallOFDutyMVP666

Cannot wait for the next video! Comment from : @have_a_nice_day399

Hi Steve, thank you for your amazing videos They spark pure joy in relearning control systems! A topic that I've studied in university and unfortunately didn't enjoyed at the time I'm looking forward to read your book "Data-Driven Science and Engineering"! Hope you and your family are safe and please keep up with the amazing work! Comment from : @gabrinegaum

That's great!!!!brThanks a lot Comment from : @AliRashidi97

brilliant Comment from : @msoulforged

Very cool! This seems like it could work well with financial time series data in various ways too Comment from : @bryan-9742

hype! Comment from : @NeuralEngin33r

Awesome Thanks! Got the hard copy of the book Data Driven Science and Engineering, very helpful Comment from : @krishnaaditya2086

very interesting Comment from : @ilyboc

YEAH ! Comment from : @billykotsos4642

Was Eagerly waiting for this prof Thanks✨ Comment from : @prajjwalsrivastav1740

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