Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations

Published 2019 View Full Article

  1. Home
  2. Publications
  3. Publication Search
  4. Publication Details
Title
Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations
Authors
Keywords
Error modeling, Supervised machine learning, High-dimensional regression, Parameterized nonlinear equations, Model reduction, ROMES method
Journal
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 348, Issue -, Pages 250-296
Publisher
Elsevier BV
Online
2019-02-05
DOI
10.1016/j.cma.2019.01.024

Ask authors/readers for more resources

Protocol

Community support

Reagent

Community support

Reprint

Contact the author

Publish scientific posters with Peeref

Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.

Learn More

Add your recorded webinar

Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.

Upload Now
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.