{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T16:17:46Z","timestamp":1756311466362,"version":"3.40.3"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031360268"},{"type":"electronic","value":"9783031360275"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-36027-5_36","type":"book-chapter","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T08:02:52Z","timestamp":1688025772000},"page":"453-468","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fixed-Budget Online Adaptive Learning for\u00a0Physics-Informed Neural Networks. Towards Parameterized Problem Inference"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0834-7186","authenticated-orcid":false,"given":"Thi Nguyen Khoa","family":"Nguyen","sequence":"first","affiliation":[]},{"given":"Thibault","family":"Dairay","sequence":"additional","affiliation":[]},{"given":"Rapha\u00ebl","family":"Meunier","sequence":"additional","affiliation":[]},{"given":"Christophe","family":"Millet","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6346-4519","authenticated-orcid":false,"given":"Mathilde","family":"Mougeot","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"unstructured":"Daw, A., Bu, J., Wang, S., Perdikaris, P., Karpatne, A.: Rethinking the importance of sampling in physics-informed neural networks. arXiv:2207.02338 (2022)","key":"36_CR1"},{"doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes. Comput. Methods Appl. Mech. Eng. 404, 115771 (2023)","key":"36_CR2","DOI":"10.1016\/j.cma.2022.115771"},{"issue":"6","key":"36_CR3","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","volume":"3","author":"GE Karniadakis","year":"2021","unstructured":"Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S., Yang, L.: Physics-informed machine learning. Nat. Rev. Phys. 3(6), 422\u2013440 (2021)","journal-title":"Nat. Rev. Phys."},{"unstructured":"Liu, Z., Yang, Y., Cai, Q.D.: Solving differential equation with constrained multilayer feedforward network. arXiv:1904.06619 (2019)","key":"36_CR4"},{"issue":"1","key":"36_CR5","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1137\/19M1274067","volume":"63","author":"L Lu","year":"2021","unstructured":"Lu, L., Meng, X., Mao, Z., Karniadakis, G.E.: DeepXDE: a deep learning library for solving differential equations. SIAM Rev. 63(1), 208\u2013228 (2021)","journal-title":"SIAM Rev."},{"issue":"8","key":"36_CR6","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1111\/mice.12685","volume":"36","author":"MA Nabian","year":"2021","unstructured":"Nabian, M.A., Gladstone, R.J., Meidani, H.: Efficient training of physics-informed neural networks via importance sampling. Comput.-Aided Civ. Infrastruct. Eng. 36(8), 962\u2013977 (2021)","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"doi-asserted-by":"crossref","unstructured":"Nguyen, T.N.K., Dairay, T., Meunier, R., Mougeot, M.: Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process. Eng. Appl. Artif. Intell. 114, 105176 (2022)","key":"36_CR7","DOI":"10.1016\/j.engappai.2022.105176"},{"key":"36_CR8","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. 378, 686\u2013707 (2019)","journal-title":"J. Comput. Phys."},{"doi-asserted-by":"crossref","unstructured":"Shin, Y., Darbon, J., Karniadakis, G.E.: On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs. arXiv:2004.01806 (2020)","key":"36_CR9","DOI":"10.4208\/cicp.OA-2020-0193"},{"doi-asserted-by":"crossref","unstructured":"Wu, C., Zhu, M., Tan, Q., Kartha, Y., Lu, L.: A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks. Comput. Methods Appl. Mech. Eng. 403, 115671 (2023)","key":"36_CR10","DOI":"10.1016\/j.cma.2022.115671"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36027-5_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T08:07:17Z","timestamp":1688026037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36027-5_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031360268","9783031360275"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36027-5_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 June 2023","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":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","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":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2023\/","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":"530","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":"188","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":"94","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":"35% - 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":"3,2","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)"}}]}}