000168605 001__ 168605
000168605 005__ 20260211123813.0
000168605 0247_ $$2doi$$a10.1016/j.compbiomed.2026.111472
000168605 0248_ $$2sideral$$a148037
000168605 037__ $$aART-2026-148037
000168605 041__ $$aeng
000168605 100__ $$aOrera, J.$$uUniversidad de Zaragoza
000168605 245__ $$aReconstructing in-vitro and in-vivo signals and parameters in networks of elastic vessels using physics-informed neural networks
000168605 260__ $$c2026
000168605 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168605 5203_ $$aThe reconstruction of waveforms and hidden parameters is crucial for the physical modeling of steady and transient flows in networks of elastic vessels (arteries), where many mechanical properties are not directly measurable. This work investigates the potential of Physics-Informed Neural Networks (PINNs) to address the challenge of reconstructing pressure and flow signals and inferring parameters from experimental data. We incorporate the zero-dimensional (0D) system of coupled differential equations that describe flow in elastic vessels into the neural network, which we call 0D-PINN. We evaluate our methodology with several test cases representing different dynamical systems, including an experimental mock arterial network with 37 silicone vessels replicating the human arterial system, as well as a clinical case based on in-vivo MRI data from a healthy adult’s thoracic aorta. It is shown that coupling 0D models with Physics-Informed Neural Networks (PINNs) enables the recovery of parameters and waveforms from experimental in-vitro or in-vivo data.
000168605 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2023-150074NB-I00
000168605 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000168605 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168605 700__ $$0(orcid)0000-0001-7056-6913$$aMairal, J.$$uUniversidad de Zaragoza
000168605 700__ $$aSánchez-Fuster, L.$$uUniversidad de Zaragoza
000168605 700__ $$0(orcid)0000-0002-1386-5543$$aMurillo, J.$$uUniversidad de Zaragoza
000168605 7102_ $$15001$$2600$$aUniversidad de Zaragoza$$bDpto. Ciencia Tecnol.Mater.Fl.$$cÁrea Mecánica de Fluidos
000168605 773__ $$g203 (2026), 111472 [19 pp.]$$pComput. biol. med.$$tComputers in biology and medicine$$x0010-4825
000168605 8564_ $$s10023144$$uhttps://zaguan.unizar.es/record/168605/files/texto_completo.pdf$$yVersión publicada
000168605 8564_ $$s2304136$$uhttps://zaguan.unizar.es/record/168605/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168605 909CO $$ooai:zaguan.unizar.es:168605$$particulos$$pdriver
000168605 951__ $$a2026-02-11-10:28:07
000168605 980__ $$aARTICLE