Model Discovery With Physics-informed Machine Learning - Data-driven Dynamics | Lecture 21
Synopsis
In the previous lecture we were introduced to the powerful and versatile method of physics-informed neural networks (PINNs).
You Might Also Like
![Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]](https://i.ytimg.com/vi/-zrY7P2dVC4/hq720.jpg?sqp=-oaymwEcCNAFEJQDSFXyq4qpAw4IARUAAIhCGAFwAcABBg==&rs=AOn4CLA3nQsCqiTdGuasMrlDowyJqfk3Tg)
![AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]](https://i.ytimg.com/vi/ARMk955pGbg/hq720.jpg?sqp=-oaymwEcCNAFEJQDSFXyq4qpAw4IARUAAIhCGAFwAcABBg==&rs=AOn4CLBMVhxazCgh0pAm1KCjDm3G5IBQXw)
![AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]](https://i.ytimg.com/vi/GCz6afDVy5Y/hq720.jpg?sqp=-oaymwEcCNAFEJQDSFXyq4qpAw4IARUAAIhCGAFwAcABBg==&rs=AOn4CLB3Ibx862klkhVLQfe1psR_Wf68vA)
Download Options
Choose a download method below. All links open in new tabs.
| Service | Features | Action |
|---|---|---|
| SaveFrom |
MP4 & MP3 • HD Quality • Browser Extension Available
|
Download |
Security Notice: These are third-party services. We recommend using antivirus software and being cautious of pop-up ads.