Uploaded September 2025 | Updated September 2026, 2 weeks ago
In this video, we explore the process of forming and testing a hypothesis, for example using significant values.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
05:01 Deep Dive: p Value
08:41 p Value and Experiment Design
12:48 Two Tail Tests
16:15 Composite and Complex Hypotheses
In this video, we explore the process of forming and testing a hypothesis, for example using significant values.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
05:01 Deep Dive: p Value
08:41 p Value and Experiment Design
12:48 Two Tail Tests
16:15 Composite and Complex Hypotheses
![AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]](https://i.ytimg.com/vi/_ObvDgPMWkU/mqdefault.jpg "AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]
This video provides a brief preview of the upcoming modules and bootcamps in this series on Physics Informed Machine Learning. Topics include: (1) Parsimonious modeling and SINDy; (2) Physics informed neural networks (PINNs); (3) Operator methods, like DeepONets and Fourier Neural Operators; (4) Symmetries in physics and machine learning; (5) Digital Twin technology; and (6) Case studies in engineering.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro & Recap
01:06 Reviewing the 5 Stages
04:08 Reviewing Physics in the Stages
05:11 Why Physical Models: Cost & Data Scale
07:53 Why Physical Models: Generalized Models
10:01 Why Physcial Models: Discovering Physics
11:40 Holistic Impact of Embedding Physics // Struggling to find a good wording here
12:55 Case Study: Pendulum Data and SINDy
15:20 Case Study: Symbolic Regression and Evolutionary Optimization
16:45 Case Study: Lagrangian Neural Networks
18:34 Architectures and Symmetries
19:36 Applications in Engineering
21:21 The Digital Twin
22:15 Benchmark Problems
23:35 Outro")
