Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion

Published 2019 View Full Article

  1. Home
  2. Publications
  3. Publication Search
  4. Publication Details
Title
Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion
Authors
Keywords
Gaussian processes, Bayesian inference, Variational inference, Uncertainty quantification, Surrogate models, Stochastic partial differential equations
Journal
JOURNAL OF COMPUTATIONAL PHYSICS
Volume 383, Issue -, Pages 166-195
Publisher
Elsevier BV
Online
2019-01-25
DOI
10.1016/j.jcp.2018.12.037

Ask authors/readers for more resources

Protocol

Community support

Reagent

Community support

Reprint

Contact the author

Discover Peeref hubs

Discuss science. Find collaborators. Network.

Join a conversation

Ask a Question. Answer a Question.

Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.

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.