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 …