{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:00:38Z","timestamp":1768341638879,"version":"3.49.0"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030504328","type":"print"},{"value":"9783030504335","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":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-50433-5_9","type":"book-chapter","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T19:03:44Z","timestamp":1592593424000},"page":"117-123","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach"],"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":[[2020,6,15]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Barwey, S., Hassanaly, M., Raman, V., Steinberg, A.: Using machine learning to construct velocity fields from OH-PLIF images. Combust. Sci. Technol. 1\u201324 (2019)","DOI":"10.1080\/00102202.2019.1678379"},{"issue":"10","key":"9_CR2","doi-asserted-by":"publisher","first-page":"100501","DOI":"10.1103\/PhysRevFluids.4.100501","volume":"4","author":"MP Brenner","year":"2019","unstructured":"Brenner, M.P.: Perspective on machine learning for advancing fluid mechanics. Phys. Rev. Fluids 4(10), 100501 (2019)","journal-title":"Phys. Rev. Fluids"},{"key":"9_CR3","doi-asserted-by":"publisher","first-page":"104604","DOI":"10.1103\/PhysRevFluids.3.104604","volume":"3","author":"P Clark Di Leoni","year":"2018","unstructured":"Clark Di Leoni, P., Mazzino, A., Biferale, L.: Inferring flow parameters and turbulent configuration with physics-informed data assimilation and spectral nudging. Phys. Rev. Fluids 3, 104604 (2018)","journal-title":"Phys. Rev. Fluids"},{"key":"9_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/978-3-030-22747-0_15","volume-title":"Computational Science \u2013 ICCS 2019","author":"NAK Doan","year":"2019","unstructured":"Doan, N.A.K., Polifke, W., Magri, L.: Physics-informed echo state networks for chaotic systems forecasting. In: Rodrigues, J.M.F., et al. (eds.) ICCS 2019. LNCS, vol. 11539, pp. 192\u2013198. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-22747-0_15"},{"key":"9_CR5","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1017\/jfm.2019.238","volume":"870","author":"K Fukami","year":"2019","unstructured":"Fukami, K., Fukagata, K., Taira, K.: Super-resolution reconstruction of turbulent flows with machine learning. J. Fluid Mech. 870, 106\u2013120 (2019)","journal-title":"J. Fluid Mech."},{"key":"9_CR6","volume-title":"Atmospheric Modeling, Data Assimilation, and Predictability","author":"E Kalnay","year":"2003","unstructured":"Kalnay, E.: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, Cambridge (2003)"},{"key":"9_CR7","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)"},{"issue":"2","key":"9_CR8","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1175\/1520-0469(1963)020<0130:DNF>2.0.CO;2","volume":"20","author":"EN Lorenz","year":"1963","unstructured":"Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20(2), 130\u2013141 (1963)","journal-title":"J. Atmos. Sci."},{"issue":"4","key":"9_CR9","doi-asserted-by":"publisher","first-page":"041102","DOI":"10.1063\/1.4979665","volume":"27","author":"Z Lu","year":"2017","unstructured":"Lu, Z., Pathak, J., Hunt, B., Girvan, M., Brockett, R., Ott, E.: Reservoir observers: model-free inference of unmeasured variables in chaotic systems. Chaos 27(4), 041102 (2017)","journal-title":"Chaos"},{"issue":"3","key":"9_CR10","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."},{"issue":"4","key":"9_CR11","doi-asserted-by":"publisher","first-page":"041101","DOI":"10.1063\/1.5028373","volume":"28","author":"J Pathak","year":"2018","unstructured":"Pathak, J., et al.: Hybrid forecasting of chaotic processes: using machine learning in conjunction with a knowledge-based model. Chaos 28(4), 041101 (2018)","journal-title":"Chaos"},{"key":"9_CR12","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi, M., Perdikaris, P., Karniadakis, G.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019)","journal-title":"J. Comput. Phys."},{"issue":"10","key":"9_CR13","doi-asserted-by":"publisher","first-page":"2228","DOI":"10.1007\/s10439-012-0579-3","volume":"40","author":"S Sankaran","year":"2012","unstructured":"Sankaran, S., Moghadam, M.E., Kahn, A.M., Tseng, A.M., Guccione, J.M.: Patient-specific multiscale modeling of blood flow for coronary artery bypass graft surgery. Ann. Biomed. Eng. 40(10), 2228\u20132242 (2012)","journal-title":"Ann. Biomed. Eng."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50433-5_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T23:08:53Z","timestamp":1718752133000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-50433-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030504328","9783030504335"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50433-5_9","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":"15 June 2020","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":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","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":"3 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2020\/","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":"230","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":"98","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":"3","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":"43% - 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":"2.5","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":"4","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":"248 workshop papers were selected from 489 submissions to the thematic tracks. The conference was canceled due to the COVID-19 pandemic.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}