Summary of Pinn-bo: a Black-box Optimization Algorithm Using Physics-informed Neural Networks, by Dat Phan-trong et al.
PINN-BO: A Black-box Optimization Algorithm using Physics-Informed Neural Networks
by Dat Phan-Trong, Hung The Tran, Alistair Shilton, Sunil Gupta
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 PINN-BO algorithm combines Physics-Informed Neural Networks (PINNs) with black-box optimization techniques to enhance the sample efficiency of global optimization in noisy and expensive black-box functions. By incorporating knowledge from Partial Differential Equations (PDEs), PINN-BO leverages domain information to improve optimization performance, outperforming existing methods on various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have developed a new way to find the best solution for complex problems by combining ideas from physics and machine learning. This method, called PINN-BO, is better than others at finding the answer quickly when trying to optimize something that’s hard to understand or requires many calculations. It works by using information about how things should behave in the real world, along with some trial and error. |
Keywords
* Artificial intelligence * Machine learning * Optimization
