{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T10:38:20Z","timestamp":1770460700476,"version":"3.49.0"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030617240","type":"print"},{"value":"9783030617257","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61725-7_18","type":"book-chapter","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T13:05:25Z","timestamp":1604322325000},"page":"143-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Machine Learning Algorithms for Improved Thermospheric Density Modeling"],"prefix":"10.1007","author":[{"given":"Herbert","family":"Turner","sequence":"first","affiliation":[]},{"given":"Maggie","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"David","family":"Gondelach","sequence":"additional","affiliation":[]},{"given":"Richard","family":"Linares","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"18_CR1","unstructured":"Bergstra, J., Yamins, D., Cox, D.D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: Proceedings of the 30th International Conference on Machine Learning, vol. 28, p. I-115-I-123. JMLR (2013)"},{"key":"18_CR2","unstructured":"Bernstein, D., Ridley, A., Cutler, J., Cohn, A.: Transformative advances in DDDAS with application to space weather monitoring. University of Michigan (2015)"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Blasch, E.: DDDAS advantages from high-dimensional simulation. In: 2018 Winter Simulation Conference (WSC), pp. 1418\u20131429. IEEE (2018)","DOI":"10.1109\/WSC.2018.8632336"},{"issue":"2","key":"18_CR4","doi-asserted-by":"publisher","first-page":"e2019SW002356","DOI":"10.1029\/2019SW002356","volume":"18","author":"DJ Gondelach","year":"2020","unstructured":"Gondelach, D.J., Linares, R.: Real-time thermospheric density estimation viatwo-line element data assimilation. Space Weather 18(2), e2019SW002356 (2020)","journal-title":"Space Weather"},{"key":"18_CR5","unstructured":"Gonzalez, F.J., Balajewicz, M.: Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems (2018). arXiv preprint arXiv:1808.01346"},{"key":"18_CR6","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"18_CR7","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015)"},{"key":"18_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980"},{"key":"18_CR9","doi-asserted-by":"publisher","first-page":"108973","DOI":"10.1016\/j.jcp.2019.108973","volume":"404","author":"K Lee","year":"2020","unstructured":"Lee, K., Carlberg, K.T.: Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders. J. Comput. Phys. 404, 108973 (2020)","journal-title":"J. Comput. Phys."},{"key":"18_CR10","unstructured":"Licata, R.J., Mehta, P.M.: Physics-informed machine learning with autoencoders and LSTM for probabilistic space weather modeling and forecasting. In: 100th American Meteorological Society Annual Meeting (2020)"},{"key":"18_CR11","unstructured":"Linares, R., Mehta, P.M., Godinez, H.C., Gondelach, D.J.: Koopman operator theory for thermospheric density modeling. In: Proceedings of the 29th AAS\/AIAA Space Flight Mechanics Meeting, Ka\u2019anapali, HI (2019)"},{"issue":"10","key":"18_CR12","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1002\/2017SW001642","volume":"15","author":"PM Mehta","year":"2017","unstructured":"Mehta, P.M., Linares, R.: A methodology for reduced order modeling and calibration of the upper atmosphere. Space Weather 15(10), 1270\u20131287 (2017)","journal-title":"Space Weather"},{"issue":"5","key":"18_CR13","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1029\/2018SW001840","volume":"16","author":"PM Mehta","year":"2018","unstructured":"Mehta, P.M., Linares, R., Sutton, E.K.: A quasi-physical dynamic reduced order model for thermospheric mass density via hermitian space-dynamic mode decomposition. Space Weather 16(5), 569\u2013588 (2018)","journal-title":"Space Weather"},{"issue":"56","key":"18_CR14","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"18_CR15","unstructured":"Szegedy, C., et al.: Going deeper with convolutions (2014). arXiv preprint arXiv:1409.4842"}],"container-title":["Lecture Notes in Computer Science","Dynamic Data Driven Applications Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61725-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T09:26:45Z","timestamp":1608802005000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61725-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030617240","9783030617257"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61725-7_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DDDAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Dynamic Data Driven Application Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Boston, MA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dddas2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/dddas-conf\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}