![AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning] @Eigensteve](https://i.ytimg.com/vi/g-S0m2zcKUg/maxresdefault.jpg)
Uploaded March 2024 | Updated September 2026, 2 weeks ago
This video discusses the second stage of the machine learning process: (2) collecting and curating training data to inform the model. There are opportunities to incorporate physics into this stage of the process, such as data augmentation to incorporate known symmetries.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
03:02 Augmenting Data with Physics
04:16 Coordinates Matter!
06:38 Simulated vs Experimental Data
10:42 Big Data vs Diverse Data
12:48 Generalizing Models with Physics
16:31 Data is Expensive
17:42 Data is Biased
18:58 Rare Events
21:24 Small Signals
24:13 Galileo Dropped the Ball
27:10 Hidden Variables
29:22 Preview: Discovering Governing Equations
30:42 The Digital Twin
35:09 Outro
This video discusses the second stage of the machine learning process: (2) collecting and curating training data to inform the model. There are opportunities to incorporate physics into this stage of the process, such as data augmentation to incorporate known symmetries.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
03:02 Augmenting Data with Physics
04:16 Coordinates Matter!
06:38 Simulated vs Experimental Data
10:42 Big Data vs Diverse Data
12:48 Generalizing Models with Physics
16:31 Data is Expensive
17:42 Data is Biased
18:58 Rare Events
21:24 Small Signals
24:13 Galileo Dropped the Ball
27:10 Hidden Variables
29:22 Preview: Discovering Governing Equations
30:42 The Digital Twin
35:09 Outro
![AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]](https://i.ytimg.com/vi/g-S0m2zcKUg/mqdefault.jpg "AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]
This video discusses the second stage of the machine learning process: (2) collecting and curating training data to inform the model. There are opportunities to incorporate physics into this stage of the process, such as data augmentation to incorporate known symmetries.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
03:02 Augmenting Data with Physics
04:16 Coordinates Matter!
06:38 Simulated vs Experimental Data
10:42 Big Data vs Diverse Data
12:48 Generalizing Models with Physics
16:31 Data is Expensive
17:42 Data is Biased
18:58 Rare Events
21:24 Small Signals
24:13 Galileo Dropped the Ball
27:10 Hidden Variables
29:22 Preview: Discovering Governing Equations
30:42 The Digital Twin
35:09 Outro")
