{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T21:36:56Z","timestamp":1773265016740,"version":"3.50.1"},"reference-count":18,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,12]]},"DOI":"10.1109\/icdmw69685.2025.00228","type":"proceedings-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T19:50:39Z","timestamp":1773172239000},"page":"1896-1904","source":"Crossref","is-referenced-by-count":0,"title":["Cluster-Weighted Training of Deep Surrogate Models for Subgrid Turbulent Transport"],"prefix":"10.1109","author":[{"given":"Rimsha Hameed","family":"Syeda","sequence":"first","affiliation":[{"name":"Georgia State University,Department of Computer Science,Atlanta,Georgia"}]},{"given":"Dustin","family":"Kempton","sequence":"additional","affiliation":[{"name":"Georgia State University,Department of Computer Science,Atlanta,Georgia"}]},{"given":"Viacheslav","family":"Sadykov","sequence":"additional","affiliation":[{"name":"Georgia State University,Department of Physics and Astronomy,Atlanta,Georgia"}]},{"given":"Irina","family":"Kitiashvili","sequence":"additional","affiliation":[{"name":"Computational Physics Branch, NASA Ames Research Center,Moffett Field,California"}]},{"given":"Rafal","family":"Angryk","sequence":"additional","affiliation":[{"name":"Georgia State University,Department of Computer Science,Atlanta,Georgia"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Reynolds Stress Modeling Using Data Driven Machine Learning Algorithms","author":"Panda","year":"2021","journal-title":"arXiv e-prints"},{"issue":"1","key":"ref2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3847\/1538-4357\/ac88cc","article-title":"Physics-informed machine learning for modeling turbulence in supernovae","volume":"940","author":"Karpov","year":"2022","journal-title":"The Astrophysical Journal"},{"key":"ref3","article-title":"Developing machine learning models of subgrid turbulent transport for quiet sun 3d radiative hydrodynamic simulations","author":"Syeda","year":"2025","journal-title":"SSRN Preprint No. 5236395"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1088\/0004-637X\/808\/1\/59"},{"key":"ref5","article-title":"Simulations of Stellar Magnetoconvection using the Radiative MHD Code \u2019StellarBox\u2019","author":"Wray","year":"2015","journal-title":"arXiv e-prints"},{"key":"ref6","first-page":"39","article-title":"Realistic Simulations of Stellar Radiative MHD","volume-title":"Variability of the Sun and Sun-Like Stars: from Asteroseismology to Space Weather","author":"Wray","year":"2018"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/BF00158429"},{"key":"ref8","article-title":"Revisiting turbulent properties of solar convection with 3D radiative hydrodynamic modeling","author":"Kitiashvili","year":"2025","journal-title":"arXiv e-prints"},{"issue":"3","key":"ref9","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1175\/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2","article-title":"General Circulation Experiments with the Primitive Equations","volume":"91","author":"Smagorinsky","year":"1963","journal-title":"Monthly Weather Review"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1063\/1.857955"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1017\/jfm.2016.615"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3847\/1538-4357\/accae2"},{"key":"ref14","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2009.09.011"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"ref17","first-page":"1027","article-title":"k-means++: The advantages of careful seeding","volume-title":"Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics","author":"Arthur","year":"2007"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3847\/1538-4357\/aabba0"}],"event":{"name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","location":"Washington, DC, USA","start":{"date-parts":[[2025,11,12]]},"end":{"date-parts":[[2025,11,15]]}},"container-title":["2025 IEEE International Conference on Data Mining Workshops (ICDMW)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11415623\/11415713\/11415926.pdf?arnumber=11415926","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T05:33:30Z","timestamp":1773207210000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11415926\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,12]]},"references-count":18,"URL":"https:\/\/doi.org\/10.1109\/icdmw69685.2025.00228","relation":{},"subject":[],"published":{"date-parts":[[2025,11,12]]}}}