{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:42:08Z","timestamp":1779255728583,"version":"3.51.4"},"reference-count":36,"publisher":"ASME International","issue":"4","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/100000147","name":"Division of Civil, Mechanical and Manufacturing Innovation","doi-asserted-by":"publisher","award":["2035038"],"award-info":[{"award-number":["2035038"]}],"id":[{"id":"10.13039\/100000147","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000147","name":"Division of Civil, Mechanical and Manufacturing Innovation","doi-asserted-by":"publisher","award":["1855651"],"award-info":[{"award-number":["1855651"]}],"id":[{"id":"10.13039\/100000147","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000209","name":"National Academy of Sciences","doi-asserted-by":"publisher","award":["Grainger Frontiers of Engineering Grant Award"],"award-info":[{"award-number":["Grainger Frontiers of Engineering Grant Award"]}],"id":[{"id":"10.13039\/100000209","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Active 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.<\/jats:p>","DOI":"10.1115\/1.4056567","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T14:30:23Z","timestamp":1671805823000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":15,"title":["Partitioned Active Learning for Heterogeneous Systems"],"prefix":"10.1115","volume":"23","author":[{"given":"Cheolhei","family":"Lee","sequence":"first","affiliation":[{"name":"Virginia Tech Grado Department of Industrial and Systems Engineering, , Blacksburg, VA 24061"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Virginia Tech Department of Materials Science and Engineering, , Blacksburg, VA 24061"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianguo","family":"Wu","sequence":"additional","affiliation":[{"name":"Peking University Department of Industrial Engineering and Management, , Beijing 100080 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Cai","sequence":"additional","affiliation":[{"name":"Virginia Tech Department of Materials Science and Engineering, , Blacksburg, VA 24061"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Yue","sequence":"additional","affiliation":[{"name":"Virginia Tech Grado Department of Industrial and Systems Engineering, , Blacksburg, VA 24061"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"2024011001100881600_CIT0001","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4939-8847-1","volume-title":"The Design and Analysis of Computer Experiments","author":"Santner","year":"2018"},{"issue":"5","key":"2024011001100881600_CIT0002","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1080\/24725854.2018.1508928","article-title":"Sequential Laplacian Regularized V-Optimal Design of Experiments for Response Surface Modeling of Expensive Tests: An Application in Wind Tunnel Testing","volume":"51","author":"Alaeddini","year":"2019","journal-title":"IISE Trans."},{"issue":"7","key":"2024011001100881600_CIT0003","doi-asserted-by":"publisher","first-page":"4604","DOI":"10.1109\/TGRS.2020.2964627","article-title":"Hyperspectral Image Classification With Convolutional Neural Network and Active Learning","volume":"58","author":"Cao","year":"2020","journal-title":"IEEE. 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