Summary of Fastlrnr and Sparse Physics Informed Backpropagation, by Woojin Cho et al.
FastLRNR and Sparse Physics Informed Backpropagation
by Woojin Cho, Kookjin Lee, Noseong Park, Donsub Rim, Gerrit Welper
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Sparse Physics Informed Backpropagation (SPInProp) is a new method for accelerating backpropagation in Low Rank Neural Representation (LRNR), a specialized neural network architecture. By exploiting LRNR’s low rank structure, SPInProp constructs a reduced approximation, FastLRNR, which is significantly smaller than the original. This reduction enables faster computation of LRNR’s backpropagation. The authors demonstrate how SPInProp accelerates the solution of parametrized partial differential equations using a physics informed neural networks framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We created a new way to make neural networks work faster by using something called Low Rank Neural Representation (LRNR). This helps with solving problems that involve physical laws, like how water moves or heat flows. The new method is called Sparse Physics Informed Backpropagation (SPInProp) and it makes the calculations much quicker. It’s like having a superpower for solving tricky math problems! |
Keywords
» Artificial intelligence » Backpropagation » Neural network
