{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:47:21Z","timestamp":1743061641536,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031539657"},{"type":"electronic","value":"9783031539664"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-53966-4_5","type":"book-chapter","created":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T18:02:29Z","timestamp":1707933749000},"page":"55-68","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Model of Two-Phase Fluid Transport Through Fractured Media: A Real-World Case Study"],"prefix":"10.1007","author":[{"given":"Leonid","family":"Sheremetov","sequence":"first","affiliation":[]},{"given":"Luis A.","family":"Lopez-Pe\u00f1a","sequence":"additional","affiliation":[]},{"given":"Gabriela B.","family":"D\u00edaz-Cortes","sequence":"additional","affiliation":[]},{"given":"Dennys A.","family":"Lopez-Falcon","sequence":"additional","affiliation":[]},{"given":"Erick E.","family":"Luna-Rojero","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"109205","DOI":"10.1016\/j.petrol.2021.109205","volume":"208","author":"MM Almajid","year":"2022","unstructured":"Almajid, M.M., Abu-Al-Saud, M.O.: Prediction of porous media fluid flow using physics informed neural networks. J. Pet. Sci. Eng. 208, 109205 (2022). https:\/\/doi.org\/10.1016\/j.petrol.2021.109205","journal-title":"J. Pet. Sci. Eng."},{"key":"5_CR2","first-page":"1","volume":"18","author":"AG Baydin","year":"2018","unstructured":"Baydin, A.G., Pearlmutter, B.A., Radul, A.A., Siskind, J.M.: Automatic differentiation in machine learning: a survey. J. March. Learn. Res. 18, 1\u201343 (2018)","journal-title":"J. March. Learn. Res."},{"key":"5_CR3","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1007\/s10409-021-01148-1","volume":"37","author":"S Cai","year":"2021","unstructured":"Cai, S., Mao, Z., Wang, Z., et al.: Physics-informed neural networks (PINNs) for fluid mechanics: a review. Acta Mech. Sin. Mech. Sin. 37, 1727\u20131738 (2021). https:\/\/doi.org\/10.1007\/s10409-021-01148-1","journal-title":"Acta Mech. Sin. Mech. Sin."},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Fraces, C.G., Papaioannou, A., Tchelepi, H.: Physics informed deep learning for transport in porous media Buckley Leverett proble (2020)","DOI":"10.2118\/203934-MS"},{"key":"5_CR5","doi-asserted-by":"publisher","unstructured":"Fraces C.G., Tchelepi, H.: Physics informed deep learning for flow and transport in porous media. (2021). https:\/\/doi.org\/10.2118\/203934-MS","DOI":"10.2118\/203934-MS"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Fuks, O., Tchelepi, H.A.: Limitations of physics informed machine learning for nonlinear two-phase transport in porous media. J. Mach. Learn. Model. Comput. 1(1), 19\u201337 (2020)","DOI":"10.1615\/JMachLearnModelComput.2020033905"},{"key":"5_CR7","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th International Conference on AI and Statistics, vol. 9, pp. 249-256 (2010)"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Han, J., Jentzen, A., Weinan, E.:.Solving high-dimensional partial differential equations using deep learning. Proc. Natl. Acad. Sci. U.S.A(2018).https:\/\/doi.org\/10.1073\/pnas.1718942115","DOI":"10.1073\/pnas.1718942115"},{"key":"5_CR9","doi-asserted-by":"publisher","first-page":"103610","DOI":"10.1016\/j.advwatres.2020.103610","volume":"141","author":"Q He","year":"2020","unstructured":"He, Q., Barajas-Solano, D., Tartakovsky, G., Tartakovsky, A.M.: Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport. Adv. Water Res. 141, 103610 (2020). https:\/\/doi.org\/10.1016\/j.advwatres.2020.103610","journal-title":"Adv. Water Res."},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"He, Q., Tartakovsky, A.M.: Physics-informed neural network method for forward and backward advection-dispersion equations. Water Res. Res. 57, e2020WR029479 (2021). https:\/\/doi.org\/10.1029\/2020WR029479","DOI":"10.1029\/2020WR029479"},{"key":"5_CR11","doi-asserted-by":"publisher","unstructured":"Jagtap, A.D., Karniadakis, G.E.: Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations, Commun. Comput. Phys., Vol.28, No.5, 2002\u20132041 (2020). https:\/\/doi.org\/10.4208\/cicp.OA-2020-0164","DOI":"10.4208\/cicp.OA-2020-0164"},{"issue":"5","key":"5_CR12","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1109\/72.712178","volume":"9","author":"IE Lagaris","year":"1998","unstructured":"Lagaris, I.E., Likas, A., Fotiadis, D.I.: Artificial neural networks for solving ordinary and partial differential equations. IEEE Trans. Neural Netw. 9(5), 987\u20131000 (1998). https:\/\/doi.org\/10.1109\/72.712178","journal-title":"IEEE Trans. Neural Netw."},{"key":"5_CR13","doi-asserted-by":"publisher","unstructured":"Mahmoudabadbozchelou, M., Karniadakis, G.E., Jamali, S.: nn-PINNs: non-Newtonian physics-informed neural networks for complex fluid modeling.\u00a0Soft Matter,\u00a018(1), 172-185 (2022). https:\/\/doi.org\/10.1039\/d1sm01298c","DOI":"10.1039\/d1sm01298c"},{"key":"5_CR14","doi-asserted-by":"publisher","unstructured":"Malik, S., Anwar, U., Ahmed, A., Aghasi, A.: Learning to solve differential equations across initial conditions (2020). https:\/\/doi.org\/10.48550\/arXiv.2003.12159","DOI":"10.48550\/arXiv.2003.12159"},{"key":"5_CR15","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.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. Comput. Phys. 378, 686\u2013707 (2019)","journal-title":"J. Comput. Phys. Comput. Phys."},{"key":"5_CR16","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1080\/00401706.1987.10488205","volume":"29","author":"M Stein","year":"1987","unstructured":"Stein, M.: Large sample properties of simulations using Latin hypercube sampling. Technometrics 29, 143\u2013151 (1987)","journal-title":"Technometrics"},{"issue":"3","key":"5_CR17","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/0307-904X(81)90039-1","volume":"5","author":"IR White","year":"1981","unstructured":"White, I.R., Lewis, R.W., Wood, W.L.: The numerical simulation of multiphase flow through a porous medium and its application to reservoir engineering. Appl. Math. Model. 5(3), 165\u2013172 (1981). https:\/\/doi.org\/10.1016\/0307-904X(81)90039-1","journal-title":"Appl. Math. Model."}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53966-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T18:03:19Z","timestamp":1707933799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53966-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031539657","9783031539664"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53966-4_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"15 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grasmere","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2023.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"In-house system and EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"119","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":"72","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":"61% - 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":"5-6","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":"1-2","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)"}}]}}