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Interface."],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:p>\n                    Aortic dissection progresses mainly via delamination of the medial layer of the wall. Notwithstanding the complexity of this process, insight has been gleaned by studying\n                    <jats:italic>in vitro<\/jats:italic>\n                    and\n                    <jats:italic>in silico<\/jats:italic>\n                    the progression of dissection driven by quasi-static pressurization of the intramural space by fluid injection, which demonstrates that the differential propensity of dissection along the aorta can be affected by spatial distributions of structurally significant interlamellar struts that connect adjacent elastic lamellae. In particular, diverse histological microstructures may lead to differential mechanical behaviour during dissection, including the pressure\u2013volume relationship of the injected fluid and the displacement field between adjacent lamellae. In this study, we develop a data-driven surrogate model of the delamination process for differential strut distributions using DeepONet, a new operator\u2013regression neural network. This surrogate model is trained to predict the pressure\u2013volume curve of the injected fluid and the damage progression within the wall given a spatial distribution of struts, with\n                    <jats:italic>in silico<\/jats:italic>\n                    data generated using a phase-field finite-element model. The results show that DeepONet can provide accurate predictions for diverse strut distributions, indicating that this composite branch-trunk neural network can effectively extract the underlying functional relationship between distinctive microstructures and their mechanical properties. More broadly, DeepONet can facilitate surrogate model-based analyses to quantify biological variability, improve inverse design and predict mechanical properties based on multi-modality experimental data.\n                  <\/jats:p>","DOI":"10.1098\/rsif.2021.0670","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T03:10:09Z","timestamp":1644376209000},"update-policy":"https:\/\/doi.org\/10.1098\/crossmark-policy","source":"Crossref","is-referenced-by-count":54,"title":["Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator\u2013regression neural network"],"prefix":"10.1098","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7861-350X","authenticated-orcid":true,"given":"Minglang","family":"Yin","sequence":"first","affiliation":[{"name":"Center for Biomedical Engineering, Brown University, Providence, RI 02912, USA"},{"name":"School of Engineering, Brown University, Providence, RI 02912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ehsan","family":"Ban","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6315-693X","authenticated-orcid":true,"given":"Bruno V.","family":"Rego","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6955-1124","authenticated-orcid":true,"given":"Enrui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Division of Applied Mathematics, Brown University, Providence, RI 02912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Cavinato","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1011-2025","authenticated-orcid":true,"given":"Jay D.","family":"Humphrey","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9713-7120","authenticated-orcid":true,"given":"George","family":"Em Karniadakis","sequence":"additional","affiliation":[{"name":"School of Engineering, Brown University, Providence, RI 02912, USA"},{"name":"Division of Applied Mathematics, Brown University, Providence, RI 02912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"175","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.matbio.2007.10.008"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-45097-z"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10237-006-0069-3"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/1538574413503561"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/app8081309"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2012.02.064"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2010.10.042"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10867-006-9027-7"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1152\/ajpheart.00908.2014"},{"key":"e_1_3_6_11_2","doi-asserted-by":"crossref","unstructured":"FitzGibbon B Fereidoonnezhad B Concannon J Hynes N Sultan S Moerman KM McGarry P. 2020 A numerical investigation of the initiation of aortic dissection. engrXiv . 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