{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T10:21:10Z","timestamp":1770459670222,"version":"3.49.0"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030779764","type":"print"},{"value":"9783030779771","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-77977-1_27","type":"book-chapter","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T07:07:25Z","timestamp":1623222445000},"page":"344-351","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Auto-Encoded Reservoir Computing for Turbulence Learning"],"prefix":"10.1007","author":[{"given":"Nguyen Anh Khoa","family":"Doan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wolfgang","family":"Polifke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Magri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"issue":"1","key":"27_CR1","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1146\/annurev-fluid-010719-060214","volume":"52","author":"SL Brunton","year":"2020","unstructured":"Brunton, S.L., Noack, B.R., Koumoutsakos, P.: Machine learning for fluid mechanics. Ann. Rev. Fluid Mech. 52(1), 477\u2013508 (2020)","journal-title":"Ann. Rev. Fluid Mech."},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Doan, N.A.K., Polifke, W., Magri, L.: Physics-informed echo state networks. J. Comput. Sci. 47, 101237 (2020)","DOI":"10.1016\/j.jocs.2020.101237"},{"key":"27_CR3","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"27_CR4","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1\u201315 (2015)"},{"key":"27_CR5","first-page":"19","volume":"32","author":"AN Kolmogorov","year":"1941","unstructured":"Kolmogorov, A.N.: Dissipation of energy in locally isotropic turbulence. Doklady Akademiia Nauk SSSR 32, 19\u201321 (1941)","journal-title":"Doklady Akademiia Nauk SSSR"},{"issue":"3","key":"27_CR6","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","volume":"3","author":"M Luko\u0161evi\u010dius","year":"2009","unstructured":"Luko\u0161evi\u010dius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127\u2013149 (2009)","journal-title":"Comput. Sci. Rev."},{"key":"27_CR7","doi-asserted-by":"publisher","first-page":"A13","DOI":"10.1017\/jfm.2019.822","volume":"882","author":"T Murata","year":"2019","unstructured":"Murata, T., Fukami, K., Fukagata, K.: Nonlinear mode decomposition with convolutional neural networks for fluid dynamics. J. Fluid Mech. 882, A13 (2019)","journal-title":"J. Fluid Mech."},{"issue":"4","key":"27_CR8","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1063\/1.858074","volume":"3","author":"N Platt","year":"1991","unstructured":"Platt, N., Sirovich, L., Fitzmaurice, N.: An investigation of chaotic Kolmogorov flows. Phys. Fluids A 3(4), 681\u2013696 (1991)","journal-title":"Phys. Fluids A"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1146\/annurev-fluid-010816-060042","volume":"49","author":"CW Rowley","year":"2017","unstructured":"Rowley, C.W., Dawson, S.T.: Model reduction for flow analysis and control. Ann. Rev. Fluid Mech. 49, 387\u2013417 (2017)","journal-title":"Ann. Rev. Fluid Mech."},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Srinivasan, P.A., Guastoni, L., Azizpour, H., Schlatter, P., Vinuesa, R.: Predictions of turbulent shear flows using deep neural networks. Phys. Rev. Fluids 4, 054603 (2019)","DOI":"10.1103\/PhysRevFluids.4.054603"},{"issue":"5","key":"27_CR11","first-page":"1","volume":"13","author":"ZY Wan","year":"2018","unstructured":"Wan, Z.Y., Vlachas, P., Koumoutsakos, P., Sapsis, T.P.: Data-assisted reduced-order modeling of extreme events in complex dynamical systems. PLoS ONE 13(5), 1\u201322 (2018)","journal-title":"PLoS ONE"},{"key":"27_CR12","doi-asserted-by":"publisher","first-page":"A51","DOI":"10.1017\/jfm.2019.905","volume":"883","author":"J Yao","year":"2020","unstructured":"Yao, J., Hussain, F.: A physical model of turbulence cascade via vortex reconnection sequence and avalanche. J. Fluid Mech. 883, A51 (2020)","journal-title":"J. Fluid Mech."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77977-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T22:02:35Z","timestamp":1749420155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77977-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030779764","9783030779771"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77977-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krakow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2021\/","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":"156","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":"48","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":"31% - 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":"2.8","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":"3.9","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)"}},{"value":"212 full and 43 short papers were selected from 479 submissions to the workshops\/ thematic tracks. The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}