Neural Implicit Flow (NIF) [Physics Informed Machine Learning]

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

%%% CHAPTERS %%%
00:00 Intro
01:25 Underlying Concept
// 02:32 Example Problem
04:36 Example Application: Turbulent Data Compression
06:23 Example Application: Sparse Sensor Placement
08:09 NIF is Mesh Agnostic
10:30 Results/Benchmark Data
// 11:00 Growing Vortices/ Cool Pictures
11:40 Shape Net Architectures
12:30 Outro

[AhmedAboElyazeed]

Hello, great explanation!

You mentioned to try and download the code but not found in the description

[XuHan-so]

I learn a lot from each of your videos. Thanks very much!

[ryanwoodall]

Reading through the article, im curious about the approach of using time as an "external" factor instead of a coordinate as in PINNs.

One reason I like the Neural ODE approach is that time is continuously varied, and therefore allows for construction of a (potentially latent) phase space where the dynamics evolve continuously. Is that possible with this approach?

[lgl_noname]

@eigensteve
TheRe is no resources found in the descrition a you had indicated they would be.

Where can those resources be found.
Please advise.
Thank you.

[arturoeugster]

Este señor es bastante malinformado de la significancia del número definido por el profesor Ackeret. También de la sequencia de Kolmogorof. 🇧🇴

[peterfarkas]

Shaowu Pan's video.
I love your content, I myself am a theoretical physicist turned CFD engineer just transitioning to ML and data science. Thank you for the educational content, it makes article discovery mush easier.