Partitioned Active Learning for Heterogeneous Systems

Simple item page
dc.contributor.authorLee, Cheolheien
dc.contributor.authorWang, Kaiwenen
dc.contributor.authorWu, Jianguoen
dc.contributor.authorCai, Wenjunen
dc.contributor.authorYue, Xiaoweien
dc.date.accessioned2023-02-07T17:16:39Zen
dc.date.available2023-02-07T17:16:39Zen
dc.date.issued2023-08en
dc.date.updated2023-02-05T04:01:23Zen
dc.description.abstractActive learning is a subfield of machine learning that focuses on improving the data collection efficiency in expensive-to-evaluate systems. Active learning-applied surrogate modeling facilitates cost-efficient analysis of demanding engineering systems, while the existence of heterogeneity in underlying systems may adversely affect the performance. In this article, we propose the partitioned active learning that quantifies informativeness of new design points by circumventing heterogeneity in systems. The proposed method partitions the design space based on heterogeneous features and searches for the next design point with two systematic steps. The global searching scheme accelerates exploration by identifying the most uncertain subregion, and the local searching utilizes circumscribed information induced by the local Gaussian process (GP). We also propose Cholesky update-driven numerical remedies for our active learning to address the computational complexity challenge. The proposed method consistently outperforms existing active learning methods in three real-world cases with better prediction and computation time.en
dc.description.versionAccepted versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.orcidYue, Xiaowei [0000-0001-6019-0940]en
dc.identifier.urihttp://hdl.handle.net/10919/113698en
dc.language.isoenen
dc.publisherASMEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titlePartitioned Active Learning for Heterogeneous Systemsen
dc.title.serialJournal of Computing and Information Science in Engineeringen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
pal_asme (2).pdf
Size:
2.31 MB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download

Collections

All Faculty Deposits
Scholarly Works, Industrial and Systems Engineering
Scholarly Works, Materials Science and Engineering (MSE)