Transfer learning for metamaterial design and simulation

Rixi Peng, Simiao Ren, Jordan Malof, Willie J. Padilla

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We demonstrate transfer learning as a tool to improve the efficacy of training deep learning models based on residual neural networks (ResNets). Specifically, we examine its use for study of multi-scale electrically large metasurface arrays under open boundary conditions in electromagnetic metamaterials. Our aim is to assess the efficiency of transfer learning across a range of problem domains that vary in their resemblance to the original base problem for which the ResNet model was initially trained. We use a quasi-analytical discrete dipole approximation (DDA) method to simulate electrically large metasurface arrays to obtain ground truth data for training and testing of our deep neural network. Our approach can save significant time for examining novel metasurface designs by harnessing the power of transfer learning, as it effectively mitigates the pervasive data bottleneck issue commonly encountered in deep learning. We demonstrate that for the best case when the transfer task is sufficiently similar to the target task, a new task can be effectively trained using only a few data points yet still achieve a test mean absolute relative error of 3% with a pre-trained neural network, realizing data reduction by a factor of 1000.

Original languageEnglish
Pages (from-to)2323-2334
Number of pages12
JournalNanophotonics
Volume13
Issue number13
DOIs
StatePublished - Mar 22 2024

Keywords

  • deep learning
  • metamaterials
  • metasurfaces
  • scattering
  • transfer learning

Fingerprint

Dive into the research topics of 'Transfer learning for metamaterial design and simulation'. Together they form a unique fingerprint.

Cite this