تقرير
AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization
| العنوان: | AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization |
|---|---|
| المؤلفون: | Grbcic, Luka, Park, Minok, Müller, Juliane, Zorba, Vassilia, de Jong, Wibe Albert |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics Physics (Other) |
| مصطلحات موضوعية: | Mathematics - Optimization and Control, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Physics - Applied Physics, Physics - Optics |
| الوصف: | Photonic surfaces designed with specific optical characteristics are becoming increasingly important for use in in various energy harvesting and storage systems. , In this study, we develop a surrogate-based optimization approach for designing such surfaces. The surrogate-based optimization framework employs the Random Forest algorithm and uses a greedy, prediction-based exploration strategy to identify the laser fabrication parameters that minimize the discrepancy relative to a user-defined target optical characteristics. We demonstrate the approach on two synthetic benchmarks and two specific cases of photonic surface inverse design targets. It exhibits superior performance when compared to other optimization algorithms across all benchmarks. Additionally, we demonstrate a technique of inverse design warm starting for changed target optical characteristics which enhances the performance of the introduced approach. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.03356 |
| رقم الأكسشن: | edsarx.2407.03356 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |