Summary of Similarity Learning with Neural Networks, by Gabriel Sanfins et al.
Similarity Learning with neural networks
by Gabriel Sanfins, Fabio Ramos, Danilo Naiff
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an); Fluid Dynamics (physics.flu-dyn)
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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 The proposed neural network algorithm automatically identifies similarity relations in data, approximating underlying physical laws that relate dimensionless quantities to their variables and coefficients. The approach is general, but demonstrated through examples in fluid mechanics, including laminar and turbulent flows in smooth and rough pipes. This framework handles both simple and intricate cases, validating its effectiveness in discovering physical laws from data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We developed a new neural network algorithm that can find patterns in data to reveal underlying physical laws. It works by finding similarities between different things, like how fluids move in pipes. We tested it on some examples of fluid flow, both simple and complicated, and it was able to figure out the rules that govern these flows. |
Keywords
» Artificial intelligence » Neural network
