WearGP: A computationally efficient machine learning framework for local erosive wear predictions via nodal Gaussian processes

Title
WearGP: A computationally efficient machine learning framework for local erosive wear predictions via nodal Gaussian processes
Authors
Keywords
Wear, Machine learning, Gaussian process, Multiphase computational fluid dynamics, Slurry pump
Journal
WEAR
Volume 422-423, Issue -, Pages 9-26
Publisher
Elsevier BV
Online
2018-12-28
DOI
10.1016/j.wear.2018.12.081

Ask authors/readers for more resources

Reprint

Contact the author

Find Funding. Review Successful Grants.

Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.

Explore

Discover Peeref hubs

Discuss science. Find collaborators. Network.

Join a conversation