Summary of Transfer Learning-assisted Inverse Modeling in Nanophotonics Based on Mixture Density Networks, by Liang Cheng and Prashant Singh and Francesco Ferranti
Transfer learning-assisted inverse modeling in nanophotonics based on mixture density networks
by Liang Cheng, Prashant Singh, Francesco Ferranti
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optics (physics.optics)
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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 Machine learning has been increasingly used to improve the simulation and design of nanophotonic structures, which rely on electromagnetic solvers for their behavior understanding. Deep neural networks have gained attention in this field, allowing for both forward and inverse modeling approaches. The paper proposes an inverse modeling method using a mixture density network model enhanced by transfer learning, capable of predicting multiple possible solutions at once, including their importance as Gaussian distributions. However, the approach requires specifying an upper bound on the number of possible simultaneous solutions in advance, jointly optimizing all parameters simultaneously can be computationally expensive and numerically unstable, and degenerate predictions may occur. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to help design nanophotonic structures. It creates a special kind of computer model that can predict many different solutions at once. This is useful because it allows researchers to quickly find the best solution for a specific problem. The method involves using something called transfer learning, which helps the model learn from previous experiments and make better predictions. |
Keywords
* Artificial intelligence * Attention * Machine learning * Transfer learning
