AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]

[@drakkhein]

Steve and team is the perfect example of knowledge transfer in academics. His papers are really an enjoyable read + this channel is informative (and practical). Thanks, Steve!

[@larrydurante9849]

So valuable Steve! Please never stop helping the rest of us gain your insight into these topics! So well thought out and delivered. Thank you, Larry

[@lukibakuoru876]

Can't wait for the next content. I have really learnt a lot

[@gebrilyoussef6851]

Prof Steve Thanks for all the efforts you put to make this Bootcamp appears on KZbin. I wish if you can say something about "Geometric Deep Learning" and "Ricci Flow"

[@marco_burderi]

Fantastic Stuff! Can't wait.

[@ESYoon-cf5eg]

Thanks for the great series of lectures, which I've been really enjoying. Around 12:00 and 13:00, it seems reference information for Raissi et al. and Lu et al. appears to be incorrect.

[@farquleetahmadkhan2710]

Anxiously waiting for it. Great #respect

[@tedmsxu]

From my native point of view, 'craft a loss function' is so similar to augment a Euler-lagrange equation with a neural network multiplier. Correct me if I'm wrong. Thank you very much for your comments.

[@FreeeStorm]

Hello Does anyone know if there is a discord or forum dedicated to physics-based machine learning?

[@ahmedhassaine3647]

I really appreciate what you are offering sir, ❤

[@mohammaddmour9127]

Waiting my dear teacher ❤❤❤❤

[@weihongs5267]

thank you steve for share your knowledge, you are talent teacher

[@idoben-yair429]

What about methods that integrate deep networks with classical methods, e.g., NN-as-preconditioner approach? Thanks for great videos!

[@alexanderskusnov5119]

Will there polyhedra and Geometric Algebra in the part 4 (symmetry)?

[@integsoft]

Thank you Professor! Any time frame for the Digital Twin module?

[@tikendraw]

I have been watching this videos as someone interested in data science . I really like these explainations, but in order to completely understand the workings we the viewers would like to see some code, some notebooks in action. DROP SOME TUTORIALS. thanks.

[@LordMichaelRahl]

Fantastic as always. Will Bayesian optimization models and methods, e.g. for materials discovery, be covered as well?

[@alihan_ozturk]

please add kolmogorov arnold networks on series

[@bithigh8301]

When I grow up I want to teach like Steve!

[@silverstreetman]

I can not wait to see the digital twins. When will you releaae that one doe digital twins?

[@antonispolykratis3283]

Looking forward...!!

[@farquleetahmadkhan2710]

Please please actual problems more on each topic and thanks #boeing

[@jaikumar848]

Hello sir ! Could you please clear my basic doubt of control theory ? If system output in time domain defined by Y(t)= r(t) * H(t) then system output will be called unstable if there is any single value t exist for which Y(t) become infinite even though for other value of t system is constant? ?

[@elromulous]

There are different types of stability. E.g. asymptotically stable en.wikipedia.org/wiki/Lyapunov_stability

[@arpitpatel5814]

You need to refer definition of stable system. Your answer is very much there. Definitions are inescapable in science because they provide the foundational language needed to describe and understand phenomena accurately.

[@murithiedwin2182]

When current AI was post infancy to crawling, you were among the few people on the globe who made made us understand the concepts of latent Spaces and sparsity in AI and ML models as you taught us practical physics and engineering with maths. Right now, you should be in tandem with the current state of advancements in AI. You should be previewing to us your models that do what you do with Physics, mathematics and ML, models that design dynamical systems all the way, from ground up to implementation all by themselves, while explaining and teaching just like you do...