AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]

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Published 2024-02-23

This video discusses the first stage of the machine learning process: (1) formulating a problem to model. There are lots of opportunities to incorporate physics into this process, and learn new physics by applying ML to the right problem.

This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company

%%% CHAPTERS %%%
00:00 Intro
04:51 Deciding on the Problem
07:08 Why do you need an ML Model?
14:54 Case Study: Super Resolution
17:07 Case Study: Discovering New Physics
18:37 Case Study: Materials Discovery
19:12 Case Study: Computational Chemistry
20:50 Case Study: Digital Twins & Discrepancy Models
21:56 Case Study: Shape Optimization
25:13 The Digital Twin
29:16 Modeling the Math
33:31 Modeling the Chaos
34:18 Case Study: Climate Modeling
35:08 Benchmark Systems
35:47 Case Study: Turbulence Closure Modeling
39:16 When not to use Machine Learning
42:15 Outro

All Comments (21)

@extrememike 5 months ago

Thanks for posting these lessons. There isn’t enough good material about this out there.
@user-nu2vl2eq8h 5 months ago

Hi professor brunton. thank you for this lectures. i am really enjoying your videos. can't wait for the next one.
@climbscience4813 5 months ago

I already love this series! I honestly think that the choice of the problem to model is BY FAR the most important one. You can bake so much prior knowledge into that alone, it can totally make or break the entire endevour.
@et4493 4 months ago

This course is one of the best learning tool on the internet. Thank you Mr Brunton
@cubedude76 5 months ago

Thanks for sharing your knowledge with us all! I feel fortunate to be able to access this level of learning for free
@FredPauling 5 months ago

This is excellent - cant wait to see the whole series
@psullivan81 5 months ago

Very interesting, can't wait to see where you take this!
@radelfalcao9327 5 months ago

Thank you for the lectures, learned/got inspired a lot.
@j.patrick9399 5 months ago

Quality content is an understatement :thanksdoc: Waiting for more
@raphaelpellegrin 5 months ago

Amazing! Thank you so much for this set of lectures!
@rito_ghosh 4 months ago

Would have really appreciated some concrete examples and case studies. With concrete math and code. I loved watching many of your videos from the databook series, because they were so unique- using math and code. And you are, always, a superb teacher, explainer. Thank you for making this series. There's really a lack of good content in this area. I really am grateful, and appreciate you doing this. Will wait for future videos. 😇😊
@blakhokisbak 1 month ago

As a Chemical Engineer that studied CFD in grad school turned Data Scientist, I absolutely love this and the fact that there is active research in the intersection of physics and AI.
@lingzhu7554 5 months ago

Thank you for this excellent lecture. Learned a lot.
@OMDMIntl 5 months ago

Thank you for doing these excellent lectures Dr Brunton
@wiredrabbit5732 5 months ago

Super stoked to see our car in the presentation 😊
@dolaponuga7553 4 months ago

Beautiful, just beautiful...Thank you
@talhazeb4021 4 months ago

Really amazed, Thank you Prof. Brunton.
@dormg22 3 months ago

Awesome lecture. God bless you for sharing this knowledge on youtube.
@ihmejakki2731 5 months ago

42:05 "... you don't want to be in the crystal energy group..." Ah, those pesky condensed matter physicists!
@raphango 4 months ago

Excellent lecture! Many thanks professor!!!