Summary of Inverse Design Of Photonic Surfaces on Inconel Via Multi-fidelity Machine Learning Ensemble Framework and High Throughput Femtosecond Laser Processing, by Luka Grbcic et al.
Inverse design of photonic surfaces on Inconel via multi-fidelity machine learning ensemble framework and high throughput femtosecond laser processing
by Luka Grbcic, Minok Park, Mahmoud Elzouka, Ravi Prasher, Juliane Müller, Costas P. Grigoropoulos, Sean D. Lubner, Vassilia Zorba, Wibe Albert de Jong
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); 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 The paper proposes a multi-fidelity machine learning ensemble framework for inverse designing photonic surfaces, which combines an initial low-fidelity model with a high-fidelity model to refine solutions through local optimization. The framework is trained on a dataset of 11,759 samples fabricated using femtosecond laser processing and is able to generate multiple sets of parameters that can produce the same target input spectral emissivity with high accuracy. SHapley Additive exPlanations analysis provides transparent model interpretability, revealing the complex relationship between laser parameters and spectral emissivity. The framework is experimentally validated by fabricating and evaluating photonic surface designs for energy harvesting devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a machine learning tool that helps design better photonic surfaces, which are important for making solar panels more efficient. It uses two different models to work together and come up with the best solution. The models are trained on data from 11,759 samples of photonic surfaces made using a special laser process. This new tool can help create more efficient energy harvesting devices. |
Keywords
» Artificial intelligence » Machine learning » Optimization
