Nonlinear Dimensionality Reduction for Low Data Regimes in Photonics Design

Efficient exploration of high-dimensional parameter space is essential in modern photonic component design. Linear dimensionality reduction such as principal component analysis has proven useful in identifying lower dimensional subspace of interest in several design problems. Yet such subspaces often exhibit curvature reflecting nonlinear relationships between design parameters. For such systems linear dimensionality reduction methods can be …