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Uncertainty Quantification"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>Obtaining accurate estimates of satellite drag coefficients in low Earth orbit is a crucial component in positioning and collision avoidance. Simulators can produce accurate estimates, but their computational expense is much too large for real-time application. A pilot study showed that Gaussian process (GP) surrogate models could accurately emulate simulations. However, cubic runtime for training GPs means that they could only be applied to a narrow range of input configurations to achieve the desired level of accuracy. In this paper we show how extensions to the local approximate GP (\\tt laGP) method allow accurate full-scale emulation. The new methodological contributions, which involve a multilevel global\/local modeling approach, and a setwise approach to local subset selection, are shown to perform well in benchmark and synthetic data settings. We conclude by demonstrating that our method achieves the desired level of accuracy, besting simpler viable (i.e., computationally tractable) global and local modeling approaches, when trained on seventy thousand core hours of drag simulations for two real-world satellites: the Hubble space telescope (HST) and the gravity recovery and climate experiment (GRACE).<\/jats:p>","DOI":"10.1137\/18m1170157","type":"journal-article","created":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T13:28:06Z","timestamp":1561642086000},"page":"720-759","source":"Crossref","is-referenced-by-count":24,"title":["Emulating Satellite Drag from Large Simulation Experiments"],"prefix":"10.1137","volume":"7","author":[{"given":"Furong","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert B.","family":"Gramacy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Haaland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Earl","family":"Lawrence","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Walker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2019,6,27]]},"reference":[{"key":"atypb1","unstructured":"J. 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Cressie (1991),\n                      Statistics for Spatial Data\n                      , rev. ed., Wiley, New York."},{"key":"atypb8","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000001437"},{"key":"atypb9","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v072.i01"},{"key":"atypb10","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2014.914442"},{"issue":"2016","key":"atypb11","first-page":"294","volume":"58","author":"Gramacy R.","year":"2015","journal-title":"Technometrics"},{"key":"atypb12","doi-asserted-by":"publisher","DOI":"10.1137\/130941912"},{"key":"atypb13","unstructured":"R. B. Gramacy and F. Sun (2018),\n                      \\tt laGP: Local Approximate Gaussian Process Regression\n                      , R package version 1.5-2."},{"key":"atypb14","doi-asserted-by":"publisher","DOI":"10.1214\/16-AOAS934"},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1214\/17-AOS1648"},{"key":"atypb16","doi-asserted-by":"publisher","DOI":"10.1214\/11-AOAS489"},{"key":"atypb17","doi-asserted-by":"publisher","DOI":"10.1029\/JA083iA06p02637"},{"key":"atypb18","unstructured":"E. Lawrence, D. Higdon, A. Walker, and M. Shoemaker (2014),\n                      Estimation for satellite collision probabilities using importance sampling\n                      , in Proceedings of the 24th AAS\/AIAA Space Flight Mechanics Meeting, Univelt, San Diego."},{"key":"atypb19","unstructured":"Y. Liu (2014),\n                      Recent Advances in Computer Experiment Modeling\n                      , Technical report, Rutgers, New Brunswick, NJ."},{"key":"atypb20","unstructured":"Y. Liu and Y. Hung (2015),\n                      Latin Hypercube Design-based Block Bootstrap for Computer Experiment Modeling\n                      , Technical report, Rutgers, New Brunswick, NJ."},{"key":"atypb21","doi-asserted-by":"publisher","DOI":"10.2514\/1.A32566"},{"key":"atypb22","doi-asserted-by":"publisher","DOI":"10.1016\/j.asr.2014.06.033"},{"key":"atypb23","doi-asserted-by":"publisher","DOI":"10.1016\/j.asr.2014.06.033"},{"key":"atypb24","unstructured":"M. Molga and C. Smutnicki (2005),\n                      Test Functions for Optimization Needs\n                      , manuscript."},{"key":"atypb25","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1993.10485320"},{"key":"atypb26","doi-asserted-by":"publisher","DOI":"10.1029\/2002JA009430"},{"key":"atypb27","unstructured":"R Core Team (2017),\n                      R: A Language and Environment for Statistical Computing\n                      , R Foundation for Statistical Computing, Vienna, Austria."},{"key":"atypb28","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v051.i01"},{"key":"atypb29","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177012413"},{"key":"atypb30","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2011.01007.x"},{"key":"atypb31","doi-asserted-by":"crossref","unstructured":"T. J. Santner, B. J. Williams, and W. I. 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Bingham (2014),\n                      Virtual Library of Simulation Experiments: Test Functions and Datasets\n                      , fromhttp:\/\/www.sfu.ca\/~ssurjano."},{"key":"atypb35","first-page":"5","volume":"21","author":"Varin C.","year":"2011","journal-title":"Statist. Sinica"},{"key":"atypb36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2015.08.006"},{"key":"atypb37","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000000241"},{"key":"atypb38","unstructured":"R. Zhang, C. Lin, and P. Ranjan (2016),\n                      Local Gaussian Process Model for Large-Scale Dynamic Computer Experiments\n                      , Technical report, Queens University, Kingston, Ontario, Canada."}],"container-title":["SIAM\/ASA Journal on Uncertainty Quantification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/epubs.siam.org\/doi\/pdf\/10.1137\/18M1170157","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T19:00:57Z","timestamp":1787338857000},"score":1,"resource":{"primary":{"URL":"https:\/\/epubs.siam.org\/doi\/10.1137\/18M1170157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["10.1137\/18M1170157"],"URL":"https:\/\/doi.org\/10.1137\/18m1170157","relation":{},"ISSN":["2166-2525"],"issn-type":[{"value":"2166-2525","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1]]}}}