209K views · Jan 8, 2017 · 5:26 · 1 HN recommender · 3 mentions · discussed 2017–2024
Two Minute Papers covers a paper on speeding up fluid and smoke simulation with convolutional networks. It contrasts the approach with earlier regression forest methods.
Why Hacker News points people here: It is cited as an early example of neural networks learning physics, and recalled during later discussions of AI video generation.
“Take a look at an application of deep learning in accelerating complex 3d rendering.”
Deep learning applied to accelerating complex 3D rendering
Take a look at an application of deep learning in accelerating complex 3d rendering. (Two Minute Papers) Stunning Video Game Graphics With Voxel Cone Tracing - this video
“Have you looked at the research of using DNN for physics simulations?”
Research using neural networks for physics simulation
Linear Logic is amazing-ly useful, link Rust [0] wouldn't exist without it. Oddly enough, I think the next discontinuity in programming language design will be in making it easier to construct anytime [1], incremental [2] and succinct [3] programs. There is a tyranny of poor tolerancing that we currently have a very hard time from escaping. Is it that differentiable programs can be made sloppy in a way that a non-differentiable one can't? Have you looked at the research of using DNN for physics simulations? [4] [0] link [1] link [2] link [3] link [4] this video
“That reminds me of an episode of '2 Minute Papers' I saw 7 years ago”
Earlier research on neural networks learning physics
That reminds me of an episode of '2 Minute Papers' I saw 7 years ago, covering research to that effect: this video
