Dimensionality Reduction in Photonics Design - New Methods and Applications

Dimensionality reduction (DR) has been an integral part of exploratory data analysis and feature selection in a multitude of machine learning applications. In particular, it has shown to be useful in multi-parameter photonics design problems where the objective function landscape offers a range of optimized designs. This talk will cover several recent advances in the methodology as well new design applications. Progress on sampling from data-efficient non-linear DR techniques will be presented, along with a method to reduce the computational load of data collection incurred by high fidelity solvers. New design problems in integrated silicon photonics as well as multijunction photonic power converters will be presented as the study cases.