{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:15:26Z","timestamp":1774678526308,"version":"3.50.1"},"reference-count":59,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12571439"],"award-info":[{"award-number":["12571439"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12271440"],"award-info":[{"award-number":["12271440"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi Province","doi-asserted-by":"publisher","award":["2025JC-YBQN-026"],"award-info":[{"award-number":["2025JC-YBQN-026"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["252300423003"],"award-info":[{"award-number":["252300423003"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016694","name":"Science and Technology Development Plan of Shandong Province","doi-asserted-by":"publisher","award":["262102211029"],"award-info":[{"award-number":["262102211029"]}],"id":[{"id":"10.13039\/100016694","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M712600"],"award-info":[{"award-number":["2022M712600"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Physics Communications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.cpc.2026.110131","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:17:27Z","timestamp":1774023447000},"page":"110131","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Fluid\u2013structure interaction training of integral conservation physics-informed neural networks for blood flow in elastic vessels"],"prefix":"10.1016","volume":"324","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9159-0690","authenticated-orcid":false,"given":"Youqiong","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2031-2015","authenticated-orcid":false,"given":"Li","family":"Cai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6126-7011","authenticated-orcid":false,"given":"Yaping","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0945-5709","authenticated-orcid":false,"given":"Qingsheng","family":"Liu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"17","key":"10.1016\/j.cpc.2026.110131_bib0001","doi-asserted-by":"crossref","first-page":"421","DOI":"10.21037\/atm.2019.07.06","article-title":"Accuracy of non-invasive and minimally invasive hemodynamic monitoring: where do we stand?","volume":"7","author":"Pour-Ghaz","year":"2019","journal-title":"Ann. Transl. Med."},{"key":"10.1016\/j.cpc.2026.110131_bib0002","doi-asserted-by":"crossref","first-page":"S67","DOI":"10.1053\/j.jvca.2019.03.043","article-title":"New developments in hemodynamic monitoring","volume":"33","author":"Scheeren","year":"2019","journal-title":"J. Cardiothorac. Vasc. Anesth."},{"issue":"6","key":"10.1016\/j.cpc.2026.110131_bib0003","doi-asserted-by":"crossref","first-page":"769","DOI":"10.3171\/jns.1982.57.6.0769","article-title":"Noninvasive transcranial doppler ultrasound recording of flow velocity in basal cerebral arteries","volume":"57","author":"Aaslid","year":"1982","journal-title":"J. Neurosurg."},{"issue":"5","key":"10.1016\/j.cpc.2026.110131_bib0004","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.jcmg.2011.03.006","article-title":"Coronary atherosclerosis imaging by coronary CT angiography: current status, correlation with intravascular interrogation and meta-analysis","volume":"4","author":"Voros","year":"2011","journal-title":"JACC-Cardiovasc. Imag."},{"key":"10.1016\/j.cpc.2026.110131_bib0005","series-title":"MRI from Picture to Proton","author":"McRobbie","year":"2017"},{"issue":"1","key":"10.1016\/j.cpc.2026.110131_bib0006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rcl.2015.09.002","article-title":"Computed tomography angiography: a review and technical update","volume":"54","author":"Fleischmann","year":"2016","journal-title":"Radiol. Clin. N. AM."},{"issue":"2","key":"10.1016\/j.cpc.2026.110131_bib0007","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/0021-9991(72)90065-4","article-title":"Flow patterns around heart valves: a numerical method","volume":"10","author":"Peskin","year":"1972","journal-title":"J. Comput. Phys."},{"issue":"1","key":"10.1016\/j.cpc.2026.110131_bib0008","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1114\/1.163","article-title":"Accuracy of computational hemodynamics in complex arterial geometries reconstructed from magnetic resonance imaging","volume":"27","author":"Moore","year":"1999","journal-title":"Ann. Biomed. Eng."},{"key":"10.1016\/j.cpc.2026.110131_bib0009","first-page":"3","article-title":"Mathematical modelling and numerical simulation of the cardiovascular system","volume":"12","author":"Quarteroni","year":"2004","journal-title":"Handb. Numer. Anal."},{"issue":"21","key":"10.1016\/j.cpc.2026.110131_bib0010","doi-asserted-by":"crossref","first-page":"7986","DOI":"10.1016\/j.jcp.2009.07.019","article-title":"Coupling biot and Navier\u2013Stokes equations for modelling fluid\u2013poroelastic media interaction","volume":"228","author":"Badia","year":"2009","journal-title":"J. Comput. Phys."},{"issue":"2","key":"10.1016\/j.cpc.2026.110131_bib0011","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.jbiomech.2011.10.020","article-title":"Validation of a 3D computational fluid\u2013structure interaction model simulating flow through an elastic aperture","volume":"45","author":"Quaini","year":"2012","journal-title":"J. Biomech."},{"issue":"6-7","key":"10.1016\/j.cpc.2026.110131_bib0012","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S0045-7825(01)00302-4","article-title":"On the coupling of 3D and 1D Navier\u2013Stokes equations for flow problems in compliant vessels","volume":"191","author":"Formaggia","year":"2001","journal-title":"Comput. Methods Appl. Mech. Eng."},{"issue":"4","key":"10.1016\/j.cpc.2026.110131_bib0013","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1137\/090772836","article-title":"Parallel algorithms for fluid-structure interaction problems in haemodynamics","volume":"33","author":"Crosetto","year":"2011","journal-title":"SIAM J. Sci. Comput."},{"issue":"3","key":"10.1016\/j.cpc.2026.110131_bib0014","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.jcp.2009.10.001","article-title":"Scalable parallel methods for monolithic coupling in fluid\u2013structure interaction with application to blood flow modeling","volume":"229","author":"Barker","year":"2010","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.cpc.2026.110131_bib0015","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.jcp.2012.08.033","article-title":"Fluid\u2013structure interaction in blood flow capturing non-zero longitudinal structure displacement","volume":"235","author":"Buka\u010d","year":"2013","journal-title":"J. Comput. Phys."},{"issue":"1","key":"10.1016\/j.cpc.2026.110131_bib0016","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s00466-008-0315-x","article-title":"Isogeometric fluid-structure interaction: theory, algorithms, and computations","volume":"43","author":"Bazilevs","year":"2008","journal-title":"Comput. Mech."},{"issue":"6-8","key":"10.1016\/j.cpc.2026.110131_bib0017","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1002\/fld.1443","article-title":"Modelling of fluid\u2013structure interactions with the space\u2013time finite elements: arterial fluid mechanics","volume":"54","author":"Tezduyar","year":"2007","journal-title":"Int. J. Numer. Methods Fluids"},{"issue":"1","key":"10.1016\/j.cpc.2026.110131_bib0018","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s10915-017-0629-y","article-title":"A higher-order discontinuous Galerkin\/arbitrary Lagrangian Eulerian partitioned approach to solving fluid\u2013structure interaction problems with incompressible, viscous fluids and elastic structures","volume":"76","author":"Wang","year":"2018","journal-title":"J. Sci. Comput."},{"issue":"1","key":"10.1016\/j.cpc.2026.110131_bib0019","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/nme.2153","article-title":"The immersed\/fictitious element method for fluid\u2013structure interaction: volumetric consistency, compressibility and thin members","volume":"74","author":"Wang","year":"2008","journal-title":"Int. J. Numer. Methods Eng."},{"issue":"2","key":"10.1016\/j.cpc.2026.110131_bib0020","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1016\/j.jcp.2007.02.017","article-title":"A numerical method for solving the 3D unsteady incompressible Navier\u2013Stokes equations in curvilinear domains with complex immersed boundaries","volume":"225","author":"Ge","year":"2007","journal-title":"J. Comput. Phys."},{"issue":"41-43","key":"10.1016\/j.cpc.2026.110131_bib0021","doi-asserted-by":"crossref","first-page":"5685","DOI":"10.1016\/j.cma.2005.11.011","article-title":"A coupled momentum method for modeling blood flow in three-dimensional deformable arteries","volume":"195","author":"Figueroa","year":"2006","journal-title":"Comput. Methods Appl. Mech. Eng."},{"issue":"1","key":"10.1016\/j.cpc.2026.110131_bib0022","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s00466-009-0450-z","article-title":"Cardiovascular flow simulation at extreme scale","volume":"46","author":"Zhou","year":"2010","journal-title":"Comput. Mech."},{"issue":"13-14","key":"10.1016\/j.cpc.2026.110131_bib0023","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1016\/j.compstruc.2011.02.019","article-title":"Fluid-structure interactions using different mesh motion techniques","volume":"89","author":"Wick","year":"2011","journal-title":"Comput. Struct."},{"key":"10.1016\/j.cpc.2026.110131_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2022.115573","article-title":"A coupled SPH-PD model for fluid\u2013structure interaction in an irregular channel flow considering the structural failure","volume":"401","author":"Sun","year":"2022","journal-title":"Comput. Methods Appl. Mech. Eng."},{"issue":"11","key":"10.1016\/j.cpc.2026.110131_bib0025","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1002\/nme.2579","article-title":"GMSH: a 3-D finite element mesh generator with built-in pre- and post-processing facilities","volume":"79","author":"Geuzaine","year":"2009","journal-title":"Int. J. Numer. Methods Eng."},{"key":"10.1016\/j.cpc.2026.110131_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2019.112732","article-title":"Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data","volume":"361","author":"Sun","year":"2020","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.cpc.2026.110131_bib0027","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.camwa.2023.11.018","article-title":"Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model","volume":"153","author":"Liu","year":"2024","journal-title":"Comput. Math. Appl."},{"key":"10.1016\/j.cpc.2026.110131_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2019.112623","article-title":"Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks","volume":"358","author":"Kissas","year":"2020","journal-title":"Comput. Methods Appl. Mech. Eng."},{"issue":"6481","key":"10.1016\/j.cpc.2026.110131_bib0029","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1126\/science.aaw4741","article-title":"Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations","volume":"367","author":"Raissi","year":"2020","journal-title":"Science"},{"issue":"12","key":"10.1016\/j.cpc.2026.110131_bib0030","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1007\/s10409-021-01148-1","article-title":"Physics-informed neural networks (PINNs) for fluid mechanics: a review","volume":"37","author":"Cai","year":"2021","journal-title":"Acta Mech. Sinica"},{"issue":"7","key":"10.1016\/j.cpc.2026.110131_bib0031","doi-asserted-by":"crossref","DOI":"10.1063\/5.0055600","article-title":"Uncovering near-wall blood flow from sparse data with physics-informed neural networks","volume":"33","author":"Arzani","year":"2021","journal-title":"Phys. Fluids"},{"key":"10.1016\/j.cpc.2026.110131_bib0032","article-title":"Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: an investigation of optimal framework based on vascular morphology","volume":"64","author":"Zhang","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"10.1016\/j.cpc.2026.110131_bib0033","doi-asserted-by":"crossref","DOI":"10.1063\/5.0188830","article-title":"Physics-informed neural networks with domain decomposition for the incompressible Navier\u2013Stokes equations","volume":"36","author":"Gu","year":"2024","journal-title":"Phys. Fluids"},{"issue":"7","key":"10.1016\/j.cpc.2026.110131_bib0034","doi-asserted-by":"crossref","first-page":"11545","DOI":"10.3934\/mbe.2023512","article-title":"Investigation on aortic hemodynamics based on physics-informed neural network","volume":"20","author":"Du","year":"2023","journal-title":"Math. Biosci. Eng."},{"issue":"2","key":"10.1016\/j.cpc.2026.110131_bib0035","doi-asserted-by":"crossref","first-page":"46","DOI":"10.3390\/fluids8020046","article-title":"Modeling of 3D blood flows with physics-informed neural networks: comparison of network architectures","volume":"8","author":"Moser","year":"2023","journal-title":"Fluids"},{"issue":"7","key":"10.1016\/j.cpc.2026.110131_bib0036","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/fluids9070153","article-title":"Three-dimensional physics-informed neural network simulation in coronary artery trees","volume":"9","author":"Alzhanov","year":"2024","journal-title":"Fluids"},{"issue":"9","key":"10.1016\/j.cpc.2026.110131_bib0037","doi-asserted-by":"crossref","first-page":"2285","DOI":"10.1109\/TMI.2022.3161653","article-title":"Physics-informed neural networks for brain hemodynamic predictions using medical imaging","volume":"41","author":"Sarabian","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/j.cpc.2026.110131_bib0038","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3390\/computation12030041","article-title":"Physically informed deep learning technique for estimating blood flow parameters in four-vessel junction after the Fontan procedure","volume":"12","author":"Isaev","year":"2024","journal-title":"Computation"},{"issue":"9","key":"10.1016\/j.cpc.2026.110131_bib0039","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.3390\/biomedicines13092058","article-title":"Physics-informed neural network for modeling the pulmonary artery blood pressure from magnetic resonance images: a reduced-order Navier\u2013Stokes model","volume":"13","author":"Jara","year":"2025","journal-title":"Biomedicines"},{"key":"10.1016\/j.cpc.2026.110131_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108081","article-title":"Performance of Fourier-based activation function in physics-informed neural networks for patient-specific cardiovascular flow","volume":"247","author":"Aghaee","year":"2024","journal-title":"Comput. Meth. Prog. Biol."},{"key":"10.1016\/j.cpc.2026.110131_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108706","article-title":"Physics-informed neural networks for parameter estimation in blood flow models","volume":"178","author":"Garay","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.cpc.2026.110131_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.cpc.2025.109569","article-title":"ICPINN: integral conservation physics-informed neural networks based on adaptive activation functions for 3D blood flow simulations","volume":"311","author":"Liu","year":"2025","journal-title":"Comput. Phys. Commun."},{"key":"10.1016\/j.cpc.2026.110131_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijheatfluidflow.2025.110011","article-title":"Integral conservation physics-informed neural networks with different network architectures for patient-specific aortic flow simulations","volume":"117","author":"Liu","year":"2026","journal-title":"Int. J. Heat Fluid Flow"},{"issue":"4","key":"10.1016\/j.cpc.2026.110131_bib0044","doi-asserted-by":"crossref","first-page":"C479","DOI":"10.1137\/23M1622696","article-title":"A meshless solver for blood flow simulations in elastic vessels using a physics-informed neural network","volume":"46","author":"Zhang","year":"2024","journal-title":"SIAM J. Sci. Comput."},{"issue":"4","key":"10.1016\/j.cpc.2026.110131_bib0045","first-page":"235","article-title":"Full 3D blood flow simulation in curved deformable vessels using physics-informed neural networks","volume":"93","author":"Zhang","year":"2024","journal-title":"Acta Math. Universitatis Comenianae"},{"key":"10.1016\/j.cpc.2026.110131_bib0046","article-title":"Parametrized sampling for 3D blood simulation in deformable vessels using physics-informed neural networks","volume":"477","author":"Zhang","year":"2025","journal-title":"J. Comput. Appl. Math."},{"issue":"3","key":"10.1016\/j.cpc.2026.110131_bib0047","doi-asserted-by":"crossref","DOI":"10.1063\/5.0259296","article-title":"Fluid\u2013structure interaction analysis of pulsatile flow in arterial aneurysms with physics-informed neural networks and computational fluid dynamics","volume":"37","author":"Ur Rehman","year":"2025","journal-title":"Phys. Fluids"},{"key":"10.1016\/j.cpc.2026.110131_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2023.112584","article-title":"Differentiable hybrid neural modeling for fluid-structure interaction","volume":"496","author":"Fan","year":"2024","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.cpc.2026.110131_bib0049","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.enganabound.2023.02.022","article-title":"A novel artificial neural network-based interface coupling approach for partitioned fluid\u2013structure interaction problems","volume":"151","author":"Mazhar","year":"2023","journal-title":"Eng. Anal. Bound. Elem."},{"key":"10.1016\/j.cpc.2026.110131_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfluidstructs.2024.104200","article-title":"A new approach for spatio-temporal interface treatment in fluid\u2013solid interaction using artificial neural networks employing coupled partitioned fluid\u2013solid solvers","volume":"131","author":"Mazhar","year":"2024","journal-title":"J. Fluids Struct."},{"key":"10.1016\/j.cpc.2026.110131_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.112834","article-title":"Review of physics-informed neural networks in hemodynamics","volume":"163","author":"Yu","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.cpc.2026.110131_bib0052","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2024.113697","article-title":"An immersed boundary method using online sequential data assimilation","volume":"524","author":"Valero","year":"2025","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.cpc.2026.110131_bib0053","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.jcp.2013.10.046","article-title":"A fully implicit domain decomposition based ALE framework for three-dimensional fluid\u2013structure interaction with application in blood flow computation","volume":"258","author":"Wu","year":"2014","journal-title":"J. Comput. Phys."},{"issue":"49-50","key":"10.1016\/j.cpc.2026.110131_bib0054","doi-asserted-by":"crossref","first-page":"4216","DOI":"10.1016\/j.cma.2008.04.018","article-title":"Modular vs. non-modular preconditioners for fluid\u2013structure systems with large added-mass effect","volume":"197","author":"Badia","year":"2008","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.cpc.2026.110131_bib0055","series-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"Glorot","year":"2010"},{"key":"10.1016\/j.cpc.2026.110131_bib0056","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.cpc.2026.110131_bib0057","series-title":"International Conference on Machine Learning","first-page":"5301","article-title":"On the spectral bias of neural networks","author":"Rahaman","year":"2019"},{"key":"10.1016\/j.cpc.2026.110131_bib0058","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1016\/j.jcp.2018.08.029","article-title":"DGM: a deep learning algorithm for solving partial differential equations","volume":"375","author":"Sirignano","year":"2018","journal-title":"J. Comput. Phys."},{"issue":"4","key":"10.1016\/j.cpc.2026.110131_bib0059","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0154517","article-title":"Fluid-structure interaction simulation of prosthetic aortic valves: comparison between immersed boundary and arbitrary Lagrangian-Eulerian techniques for the mesh representation","volume":"11","author":"Bavo","year":"2016","journal-title":"PLoS ONE"}],"container-title":["Computer Physics Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S001046552600113X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S001046552600113X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T03:18:33Z","timestamp":1774667913000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S001046552600113X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":59,"alternative-id":["S001046552600113X"],"URL":"https:\/\/doi.org\/10.1016\/j.cpc.2026.110131","relation":{},"ISSN":["0010-4655"],"issn-type":[{"value":"0010-4655","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Fluid\u2013structure interaction training of integral conservation physics-informed neural networks for blood flow in elastic vessels","name":"articletitle","label":"Article Title"},{"value":"Computer Physics Communications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cpc.2026.110131","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110131"}}