1 HN user recommended this explainer1 month2017–2024score 1
Years with an HN recommendation, 2007–2026
It is cited as an early example of neural networks learning physics, and recalled during later discussions of AI video generation.
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.
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]
Deep learning applied to accelerating complex 3D rendering
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]
Research using neural networks for physics simulation
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3 Hacker News comments
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