{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:38:05Z","timestamp":1743028685283,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030787097"},{"type":"electronic","value":"9783030787103"}],"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\/978-3-030-78710-3_46","type":"book-chapter","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T18:04:00Z","timestamp":1623953040000},"page":"482-492","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology"],"prefix":"10.1007","author":[{"given":"Victoriya","family":"Kashtanova","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ibrahim","family":"Ayed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Cedilnik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Gallinari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maxime","family":"Sermesant","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"issue":"11","key":"46_CR1","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1109\/TPAMI.2013.86","volume":"35","author":"MA Alvarez","year":"2013","unstructured":"Alvarez, M.A., Luengo, D., Lawrence, N.D.: Linear latent force models using gaussian processes. IEEE Pattern Anal. Math. Intell. 35(11), 2693\u20132705 (2013)","journal-title":"IEEE Pattern Anal. Math. Intell."},{"key":"46_CR2","unstructured":"Ayed, I., de B\u00e9zenac, E., Pajot, A., Brajard, J., Gallinari, P.: Learning dynamical systems from partial observations. arXiv preprint:1902.11136 (2019)"},{"key":"46_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-030-21949-9_7","volume-title":"Functional Imaging and Modeling of the Heart","author":"I Ayed","year":"2019","unstructured":"Ayed, I., Cedilnik, N., Gallinari, P., Sermesant, M.: EP-net: learning cardiac electrophysiology models for physiology-based constraints in data-driven predictions. In: Coudi\u00e8re, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds.) FIMH 2019. LNCS, vol. 11504, pp. 55\u201363. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-21949-9_7"},{"key":"46_CR4","unstructured":"Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. arXiv preprint:1506.03099 (2015)"},{"key":"46_CR5","unstructured":"Chen, R.T.Q., Rubanova, Y., Bettencourt, J., Duvenaud, D.: Neural ordinary differential equations. In: Proceedings of Neural Information Processing Systems (2018)"},{"issue":"417\u2013452","key":"46_CR6","first-page":"121","volume":"1","author":"JP Crutchfield","year":"1987","unstructured":"Crutchfield, J.P., McNamara, B.: Equations of motion from a data series. Complex Syst. 1(417\u2013452), 121 (1987)","journal-title":"Complex Syst."},{"key":"46_CR7","doi-asserted-by":"crossref","unstructured":"Fresca, S., Manzoni, A., Ded\u00e8, L., Quarteroni, A.: Deep learning-based reduced order models in cardiac electrophysiology. PLOS ONE 15(10), e0239416 (2020)","DOI":"10.1371\/journal.pone.0239416"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"46_CR9","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint:1412.6980 (2014)"},{"key":"46_CR10","doi-asserted-by":"publisher","first-page":"108925","DOI":"10.1016\/j.jcp.2019.108925","volume":"399","author":"Z Long","year":"2019","unstructured":"Long, Z., Lu, Y.Y., Dong, B.: PDE-net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network. J. Comput. Phys. 399, 108925 (2019)","journal-title":"J. Comput. Phys."},{"key":"46_CR11","unstructured":"Long, Z., Lu, Y., Ma, X., Dong, B.: PDE-net: Learning PDEs from data. In: International Conference on ICML, pp. 3208\u20133216. PMLR (2018)"},{"key":"46_CR12","unstructured":"Mansi, T., Passerini, T., Comaniciu, D.: Artificial Intelligence for Computational Modeling of the Heart. Elsevier (2020)"},{"issue":"5","key":"46_CR13","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/S0092-8240(03)00041-7","volume":"65","author":"CC Mitchell","year":"2003","unstructured":"Mitchell, C.C., Schaeffer, D.G.: A two-current model for the dynamics of cardiac membrane. Bull. Math. Biol. 65(5), 767\u2013793 (2003)","journal-title":"Bull. Math. Biol."},{"key":"46_CR14","doi-asserted-by":"publisher","unstructured":"Nelles, O.: Nonlinear System Identification. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/978-3-662-04323-3","DOI":"10.1007\/978-3-662-04323-3"},{"issue":"1","key":"46_CR15","first-page":"932","volume":"19","author":"M Raissi","year":"2018","unstructured":"Raissi, M.: Deep hidden physics models: deep learning of nonlinear partial differential equations. J. Mach. Learn. Res. 19(1), 932\u2013955 (2018)","journal-title":"J. Mach. Learn. Res."},{"key":"46_CR16","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/j.jcp.2017.07.050","volume":"348","author":"M Raissi","year":"2017","unstructured":"Raissi, M., Perdikaris, P., Karniadakis, G.E.: Machine learning of linear differential equations using gaussian processes. J. Comput. Phys. 348, 683\u2013693 (2017)","journal-title":"J. Comput. Phys."},{"key":"46_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/978-3-642-33418-4_5","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2012","author":"S Rapaka","year":"2012","unstructured":"Rapaka, S., et al.: LBM-EP: Lattice-Boltzmann method for fast cardiac electrophysiology simulation from 3D images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 33\u201340. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33418-4_5"},{"issue":"3","key":"46_CR18","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1098\/rsfs.2010.0041","volume":"1","author":"J Relan","year":"2011","unstructured":"Relan, J., et al.: Coupled personalization of cardiac electrophysiology models for prediction of ischaemic ventricular tachycardia. Interface Focus 1(3), 396\u2013407 (2011)","journal-title":"Interface Focus"},{"issue":"4","key":"46_CR19","doi-asserted-by":"publisher","first-page":"e1602614","DOI":"10.1126\/sciadv.1602614","volume":"3","author":"SH Rudy","year":"2017","unstructured":"Rudy, S.H., Brunton, S.L., Proctor, J.L., Kutz, J.N.: Data-driven discovery of partial differential equations. Sci. Adv. 3(4), e1602614 (2017)","journal-title":"Sci. Adv."},{"key":"46_CR20","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1016\/j.jcp.2018.08.029","volume":"375","author":"J Sirignano","year":"2018","unstructured":"Sirignano, J., Spiliopoulos, K.: DGM: a deep learning algorithm for solving partial differential equations. J. Comput. Phys. 375, 1339\u20131364 (2018)","journal-title":"J. Comput. Phys."},{"key":"46_CR21","unstructured":"Willard, J.D., Jia, X., Xu, S., Steinbach, M., Kumar, V.: Integrating physics-based modeling with machine learning: a survey. arXiv preprint:2003.04919 (2020)"},{"issue":"2217","key":"46_CR22","first-page":"20180305","volume":"474","author":"S Zhang","year":"2018","unstructured":"Zhang, S., Lin, G.: Robust data-driven discovery of governing physical laws with error bars. Proc. R. Soc. Math. Phys. Eng. Sci. 474(2217), 20180305 (2018)","journal-title":"Proc. R. Soc. Math. Phys. Eng. Sci."}],"container-title":["Lecture Notes in Computer Science","Functional Imaging and Modeling of the Heart"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78710-3_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:28:43Z","timestamp":1710264523000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78710-3_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030787097","9783030787103"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78710-3_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"18 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FIMH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Functional Imaging and Modeling of the Heart","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Stanford, CA","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fimh2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/fimh2021.github.io\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"68","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":"65","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":"0","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":"96% - 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":"2-4","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","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":"The conference took place virtually 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)"}}]}}