Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]
49,080
Published 2024-05-29
Original PINNs paper: www.sciencedirect.com/science/article/abs/pii/S002…
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi P. Perdikaris, G.E. Karniadakis
Journal of Computational Physics
Volume 378: 686-707, 2019
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
01:54 PINNs: Central Concept
06:38 Advantages and Disadvantages
11:39 PINNs and Inference
15:23 Recommended Resources
19:33 Extending PINNs: Fractional PINNs
21:40 Extending PINNs: Delta PINNs
25:33 Failure Modes
29:40 PINNs & Pareto Fronts
31:57 Outro
All Comments (21)
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The way of teaching is highly beneficial and outstanding. Thank you, Steven!
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This is hands down one of the best videos I've seen on YouTube. Great work, keep it up!
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Hi Prof. Brunton, I am a Ph.D. student from UT Austin majoring in Mechanical Engineering with specification of dynamical system and control. Your vedios has been helping me by either giving me a deeper understanding of foundamental knowledge or openning my horizon, ever since I begin my Ph.D. Just want to express my great gratitude to you again and hope I can meet you in certain conferences so that I can say thank you to you in person.
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Steve your videos are always helpful, clear and concise. Thank you so much for such amazing content. You are my hero
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Very helpful Steven. I work in consciousness studies and find too often the math is written off as too complicated. On the other side, many computational scientists may write off consciousness studies as too ethereal to be of much value. Bridging these two worlds with insight and rigor, I feel advances our understanding of both artificial and human intelligence. You have contributed to this effort here. Thank you.
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Thank you, Professor.😃
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This is really helpful, if only you posted this sooner! Thanks <3
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I was waiting for this, hope to see more about this subjects, thanks a lot.
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Awesome video again Steve. Thanks so much.
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Thank you very much for the lecture. I am looking forward for your next lecture on this topic.
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I would love to see a video on Universal ODEs (which leverages auto-diff through diffEQ solvers). Chris Rackauckas' work in the Julia language on these methods has been striking - would love to see your take on it.
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Nice, kinda gives me ideas to mix control theories together.
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Hi Professor Steve. I’d love to see a series on Transformers. Thanks for your content, greetings from Brazil.
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Great video. Thank you.
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fantastic! As always
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Great video! it may be useful to do another video about Neural Operators. It is more stable and faster in many physical tasks as i know.
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Loved the video ❤️❤️
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Wooooooow i am back to class Physics Maths Chemistry Electrical Control Systems
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very nice!
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