{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T02:01:28Z","timestamp":1780624888079,"version":"3.54.1"},"reference-count":31,"publisher":"Walter de Gruyter GmbH","license":[{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_bmt-2025-0344_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To predict abnormal pulmonary artery hemodynamics caused by ventricular septal defect (VSD) using Physics-Informed Neural Networks (PINN) and address the challenges of high computational cost in traditional Computational Fluid Dynamics (CFD) and difficulty in obtaining measurement data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2025-0344_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The PINN model was trained using boundary conditions and scattered clinical CFD data, with dynamic weighting factors incorporated to enhance training efficiency and optimize predictions. Model outputs for blood flow velocity and pressure were subsequently evaluated against CFD simulation results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2025-0344_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The PINN accurately reproduced velocity and pressure fields across pulmonary artery models using only boundary conditions and sparse internal measurements. For velocity prediction, the average RMSE, MAE, and MRE for components u, v, and w ranged from 0.274 to 0.832\u202f%, 0.448\u20131.096\u202f%, and 0.833\u20131.341\u202f%, respectively. For pressure prediction, the average RMSE, MAE, and MRE ranged from 2.953 to 5.145\u202f%, 3.264\u20135.679\u202f%, and 0.376\u20130.565\u202f%, respectively. These findings demonstrate that the framework generalizes well and provides reliable hemodynamic estimation with limited input data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2025-0344_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The PINN model compensates for incomplete measurement data through physical constraints, enabling rapid and accurate prediction of pulmonary artery hemodynamics and offering a promising non-invasive alternative for pulmonary artery pressure measurement.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/bmt-2025-0344","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:30:52Z","timestamp":1780623052000},"source":"Crossref","is-referenced-by-count":0,"title":["Physics-informed neural network-based simulation of pulmonary arterial hemodynamics abnormalities"],"prefix":"10.1515","author":[{"given":"Ziteng","family":"Lu","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Manufacturing Technology of Ministry of Education , Guizhou University , Guiyang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haisong","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Manufacturing Technology of Ministry of Education , Guizhou University , Guiyang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lanping","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Shanghai Children\u2019s Medical Center , Affiliated to Shanghai Jiao Tong University School of Medicine , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haibin","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Manufacturing Technology of Ministry of Education , Guizhou University , Guiyang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Manufacturing Technology of Ministry of Education , Guizhou University , Guiyang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"2026060501304884777_j_bmt-2025-0344_ref_001","doi-asserted-by":"crossref","unstructured":"Sahay, S, Chakinala, MM, Kim, NH, Preston, IR, Thenappan, T, McLaughlin, VV, et al.. 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