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New video series: Physics Informed Machine Learning! Physics may be embedded into AI/ML in 5 stages: 1 choose what to Model 2 curate training Data 3 design an Architecture 4 craft a Loss Function, and 5 implement Optimization Algorithm to train the model

Steven Brunton

55,759 subscribers

78,432 просмотров • 2 лет назад •via X (Twitter)

Наука и технологии Образование

Anya Rossi• Live Now

Private livecam show

Комментарии: 10

Фото профиля vik

vik2 лет назад

love the production quality on this!

Фото профиля James O'Reilly

James O'Reilly2 лет назад

Watched this in bed last night, absolutely fascinating stuff @eigensteve. These are exciting times for us engineers in the commercial space. Change is happening fast! I'm excited for the rest of these sessions.

Фото профиля deewakar

deewakar2 лет назад

Started working on this few months ago. This was amazing as usual. Looking forward for DeepOnets

Фото профиля 𝕽𝖎𝖙𝖔 𝕲𝖍𝖔𝖘𝖍 (𝖊/λ🧠⚗️🧑🏻‍💻)

𝕽𝖎𝖙𝖔 𝕲𝖍𝖔𝖘𝖍 (𝖊/λ🧠⚗️🧑🏻‍💻)2 лет назад

Waiting for the next video, already!

Фото профиля VivaDoyers

VivaDoyers2 лет назад

Thanks Professor. Love your videos and will be watching this one!

Фото профиля Stefano

Stefano2 лет назад

“Welcome back”

Фото профиля Prach na knihovně

Prach na knihovně2 лет назад

That's great! Love your work! Greetings from the Central Europe!

Фото профиля kasim guventurk

kasim guventurk2 лет назад

Thank you for the great content Professor. I am so grateful.

Фото профиля Serendipity

Serendipity2 лет назад

cant wait!

Фото профиля Johan Botha

Johan Botha2 лет назад

Thanks, Steve Although I haven't watched the complete series the parts I have watched are brilliant. Just a question how would this relate to Cyber-Physical Systems

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🚨 ELON MUSK: "It's incredibly important for AI model to be grounded in reality. Reality, you know, physics is the law and everything else is a recommendation. For AI to really be intelligent it's got to make predictions that are inline with reality, in other words, physics."

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