PofoliaShared via Pofolia

arXiv (Cornell University)· 2016· Preprint

Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin

Short summary

A novel convolutional network, trained via unsupervised learning, solves the linear system in Eulerian fluid simulations 10x faster than standard methods, enabling real-time 2D and 3D results.

AI-generated from the title and abstract; the full text is not read.

TakeawaysIn the app
Key pointsIn the app
Ask the paperIn the app

The rest is in the Pofolia app

Takeaways, key points and questions to the paper; new summaries every day for your field. Free.

Sign in on the web to open

Field: Computer Graphics and Computer-Aided Design

Computer Graphics and Computer-Aided DesignComputer Science