Reduced-space Gaussian Process Regression for data-driven probabilistic forecast of chaotic dynamical systems

Published 2017 View Full Article

  1. Home
  2. Publications
  3. Publication Search
  4. Publication Details
Title
Reduced-space Gaussian Process Regression for data-driven probabilistic forecast of chaotic dynamical systems
Authors
Keywords
Data-driven prediction, Uncertainty quantification, Order-reduction, Gaussian Process Regression, T21 barotropic climate model, Lorenz 96
Journal
PHYSICA D-NONLINEAR PHENOMENA
Volume 345, Issue -, Pages 40-55
Publisher
Elsevier BV
Online
2017-01-06
DOI
10.1016/j.physd.2016.12.005

Ask authors/readers for more resources

Protocol

Community support

Reagent

Community support

Reprint

Contact the author

Create your own webinar

Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.

Create Now

Become a Peeref-certified reviewer

The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.

Get Started
Webinars Posters Questions Hubs Funding Journals Articles Connect Collections Reviewers About Us FAQs Mobile App Privacy Policy Terms of Use
© Peeref 2019-2024. All rights reserved.