{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T19:44:56Z","timestamp":1782675896565,"version":"3.54.5"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032299147","type":"print"},{"value":"9783032299154","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/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":[[2026]]},"DOI":"10.1007\/978-3-032-29915-4_15","type":"book-chapter","created":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T19:19:21Z","timestamp":1782674361000},"page":"181-188","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Latent Attention on\u00a0Masked Patches for\u00a0Flow Reconstruction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0009-8594-082X","authenticated-orcid":false,"given":"Ben","family":"Eze","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0657-2611","authenticated-orcid":false,"given":"Luca","family":"Magri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-0597-8326","authenticated-orcid":false,"given":"Andrea","family":"N\u00f3voa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,29]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","first-page":"A7","DOI":"10.1017\/jfm.2025.10858","volume":"1024","author":"E Bekoglu","year":"2025","unstructured":"Bekoglu, E., Bempedelis, N., Steiros, K.: Formation of turbulent secondary vortex street in absence of vortex shedding instability. J. Fluid Mech. 1024, A7 (2025)","journal-title":"J. Fluid Mech."},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1146\/annurev.fl.25.010193.002543","volume":"25","author":"G Berkooz","year":"1993","unstructured":"Berkooz, G., et al.: The proper orthogonal decomposition in the analysis of turbulent flows. Annu. Rev. Fluid Mech. 25, 539\u2013575 (1993)","journal-title":"Annu. Rev. Fluid Mech."},{"key":"15_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2023.112581","volume":"496","author":"S Cheng","year":"2024","unstructured":"Cheng, S., Liu, C., Guo, Y., Arcucci, R.: Efficient deep data assimilation with sparse observations and time-varying sensors. J. Comput. Phys. 496, 112581 (2024)","journal-title":"J. Comput. Phys."},{"key":"15_CR4","unstructured":"Dosovitskiy, A.: An image is worth 16 $$\\times $$ 16 words: transformers for image recognition at scale. In: Proceedings of IEEE\/CVF CVPR, pp. 45\u201367 (2021)"},{"issue":"8","key":"15_CR5","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1364\/JOSAA.12.001657","volume":"12","author":"R Everson","year":"1995","unstructured":"Everson, R., Sirovich, L.: Karhunen-lo\u00e8ve procedure for gappy data. J. Opt. Soc. Am. A 12(8), 1657\u20131664 (1995)","journal-title":"J. Opt. Soc. Am. A"},{"key":"15_CR6","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":"15_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of IEEE\/CVF CVPR, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"issue":"6677","key":"15_CR8","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1126\/science.adi2336","volume":"382","author":"R Lam","year":"2023","unstructured":"Lam, R., et al.: Learning skillful medium-range global weather forecasting. Science 382(6677), 1416\u20131421 (2023)","journal-title":"Science"},{"issue":"12","key":"15_CR9","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s00348-017-2456-1","volume":"58","author":"Y Lee","year":"2017","unstructured":"Lee, Y., Yang, H., Yin, Z.: Piv-DCNN: cascaded deep convolutional neural networks for particle image velocimetry. Exp. Fluids 58(12), 171 (2017)","journal-title":"Exp. Fluids"},{"key":"15_CR10","doi-asserted-by":"publisher","DOI":"10.1017\/dce.2026.10038","volume":"7","author":"Y Mo","year":"2026","unstructured":"Mo, Y., Magri, L.: Reconstruction of three-dimensional turbulent flows from sparse and noisy planar measurements: a weight-sharing neural network approach. Data-Centric Eng. 7, e5 (2026)","journal-title":"Data-Centric Eng."},{"key":"15_CR11","doi-asserted-by":"publisher","DOI":"10.1017\/dce.2024.31","volume":"5","author":"Y Mo","year":"2024","unstructured":"Mo, Y., Traverso, T., Magri, L.: Decoder decomposition for the analysis of the latent space of nonlinear autoencoders with wind-tunnel experimental data. Data-Centric Eng. 5, e38 (2024)","journal-title":"Data-Centric Eng."},{"issue":"6","key":"15_CR12","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevFluids.9.064601","volume":"9","author":"L Nista","year":"2024","unstructured":"Nista, L., et al.: Influence of adversarial training on super-resolution turbulence reconstruction. Phys. Rev. Fluids 9(6), 064601 (2024)","journal-title":"Phys. Rev. Fluids"},{"key":"15_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2025.118600","volume":"450","author":"E \u00d6zalp","year":"2026","unstructured":"\u00d6zalp, E., N\u00f3voa, A., Magri, L.: Real-time forecasting of chaotic dynamics from sparse data and autoencoders. Comput. Methods Appl. Mech. Eng. 450, 118600 (2026)","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Peebles, W., Xie, S.: Scalable diffusion models with transformers. In: Proceedings of IEEE\/CVF ICCV, pp. 4172\u20134182 (2023)","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"15_CR15","doi-asserted-by":"publisher","DOI":"10.1017\/dce.2025.10022","volume":"6","author":"M Quattromini","year":"2025","unstructured":"Quattromini, M., Bucci, M.A., Cherubini, S., Semeraro, O.: Mean flow data assimilation using physics-constrained graph neural networks. Data-Centric Eng. 6, e48 (2025)","journal-title":"Data-Centric Eng."},{"key":"15_CR16","doi-asserted-by":"publisher","first-page":"A2","DOI":"10.1017\/jfm.2023.716","volume":"975","author":"A Racca","year":"2023","unstructured":"Racca, A., Doan, N.A.K., Magri, L.: Predicting turbulent dynamics with the convolutional autoencoder echo state network. J. Fluid Mech. 975, A2 (2023)","journal-title":"J. Fluid Mech."},{"issue":"3\u20134","key":"15_CR17","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1007\/BF01053745","volume":"65","author":"T Sauer","year":"1991","unstructured":"Sauer, T., Yorke, J.A., Casdagli, M.: Embedology. J. Stat. Phys. 65(3\u20134), 579\u2013616 (1991)","journal-title":"J. Stat. Phys."},{"key":"15_CR18","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"15_CR19","doi-asserted-by":"publisher","first-page":"A17","DOI":"10.1017\/jfm.2024.69","volume":"981","author":"C Xia","year":"2024","unstructured":"Xia, C., Zhang, J., Kerrigan, E.C., Rigas, G.: Active flow control for bluff body drag reduction using reinforcement learning with partial measurements. J. Fluid Mech. 981, A17 (2024)","journal-title":"J. Fluid Mech."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2026 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-29915-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T19:19:24Z","timestamp":1782674364000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/10.1007\/978-3-032-29915-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032299147","9783032299154"],"references-count":19,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-032-29915-4_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"29 June 2026","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":"Hamburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.iccs-meeting.org\/iccs2026\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}