Solving the Nonlinear Schrödinger Equation in Optical Fibers Using Physics-informed Neural Network

Xiaotian Jiang, Danshi Wang, Qirui Fan, Min Zhang, Chao Lu, Alan Pak Tao Lau

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

We constructed a physics-informed neural network (PINN) to solve the nonlinear Schrödinger equation for different input waveforms. Results show that PINN can accurately characterize pulse evolution in fibers with less complexity to SSFM methods.

Original languageEnglish
Title of host publication2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781943580866
Publication statusPublished - Jun 2021
Event2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - San Francisco, United States
Duration: 6 Jun 202111 Jun 2021

Publication series

Name2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings

Conference

Conference2021 Optical Fiber Communications Conference and Exhibition, OFC 2021
Country/TerritoryUnited States
CitySan Francisco
Period6/06/2111/06/21

ASJC Scopus subject areas

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

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