Nonlinear Dimensionality Reduction for Low Data Regimes in Photonics Design
- Resource Type
- Conference
- Authors
- Grinberg, Yuri; Al-Digeil, Muhammad; Kamandar Dezfouli, Mohsen; Melati, Daniele; Schmid, Jens H.; Cheben, Pavel; Janz, Siegfried; Xu, Danxia
- Source
- 2022 Photonics North (PN) Photonics North (PN), 2022. :1-1 May, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Photonics and Electrooptics
Transportation
Dimensionality reduction
Neural networks
Photonics
Principal component analysis
dimensionality reduction
autoencoder
small data
nanophotonic design
silicon photonics
machine learning
- Language
- ISSN
- 2693-8316
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 suboptimal. We discuss how an appropriate architecture for an autoencoder neural network along with a numerically robust initialization, show improved performance compared to linear methods even in low data regimes, which are typical for photonic design problems.