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Physics-Informed Gaussian Process Regression for Optical Fiber Communication Systems

We present a framework for enhancing Gaussian process regression machine learning models with a priori knowledge derived from models of the transmission physics in optical networks. This is done by framing the regression problem as multi-task learning, in which both the measured data and targets derived from a physical model of the system are used to optimise the kernel hyperparameters. We discuss the theoretical assumptions made and the validity of the approach. It is demonstrated that physics-informed Gaussian processes facilitate Bayesian inference with fewer data points than standard Gaussian processes, opening up application areas in which measurements are expensive. The transparency, interpretability and explainability of the proposed technique and the subsequent increased likelihood of adoption by industry are discussed.

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Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.39 No.21 November 1 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 6833-6844

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

GAUSSIAN PROCESSES
OPTICAL FIBER COMMUNICATION
EXPALINABLE MACHINE LEARNING
DATA-CENTRIC ENGINEERING

Info Detail Spesifik

10.1109/JLT.2021.3106714

Pernyataan Tanggungjawab

Josh W. Nevin, F. J. Vaquero-Caballero, David. J. Ives, Seb J. Savory

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