Combining Bayesian Optimization, SVD and Machine Learning for Advanced Optical Design

The design and optimization of optical components, such as Bragg gratings, are critical for applications in telecommunications, sensing, and photonic circuits. To overcome the limitations of traditional design methods that rely heavily on computationally intensive simulations and large datasets, we propose an integrated methodology that significantly reduces these burdens while maintaining high accuracy in predicting optical response. First, we employ a Bayesian optimization technique to strategically select a limited yet informative number of simulation points from the design space, ensuring that each contributes maximally to the model's performance. Second, we utilize singular value decomposition to effectively parametrize the entire reflectance spectra into a reduced set of coefficients, allowing us to capture all significant spectral features without losing crucial information. Finally, we apply XGBoost, a robust machine learning algorithm, to predict the entire reflectance spectra from the reduced dataset. The combination of Bayesian optimization for data selection, SVD for full-spectrum fitting, and XGBoost for predictive modeling provides a powerful, generalizable framework for the design of optical components.