Machine Learning for Modal Analysis

Pawel Strzebonski, Kent Choquette

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We train autoencoder artificial neural networks on simulated multimodal images and their corresponding modal power coefficients to reconstruct the component modal profiles and obtain a network capable of modal decomposition.

Original languageEnglish (US)
Title of host publication2020 IEEE Photonics Conference, IPC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728158914
DOIs
StatePublished - Sep 2020
Event2020 IEEE Photonics Conference, IPC 2020 - Virtual, Vancouver, Canada
Duration: Sep 28 2020Oct 1 2020

Publication series

Name2020 IEEE Photonics Conference, IPC 2020 - Proceedings

Conference

Conference2020 IEEE Photonics Conference, IPC 2020
CountryCanada
CityVirtual, Vancouver
Period9/28/2010/1/20

Keywords

  • Laser modes
  • Machine learning
  • Statistical learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

Fingerprint Dive into the research topics of 'Machine Learning for Modal Analysis'. Together they form a unique fingerprint.

Cite this