000153155 001__ 153155
000153155 005__ 20251017144552.0
000153155 0247_ $$2doi$$a10.1016/j.cma.2025.117933
000153155 0248_ $$2sideral$$a143632
000153155 037__ $$aART-2025-143632
000153155 041__ $$aeng
000153155 100__ $$aOrera, J.$$uUniversidad de Zaragoza
000153155 245__ $$aRoePINNs: An integration of advanced CFD solvers with Physics-Informed Neural Networks and application in arterial flow modeling
000153155 260__ $$c2025
000153155 5203_ $$aThe characterization of forward and inverse problems describing blood flow dynamics plays a decisive role in numerous biomedical applications. These systems can be modeled using one-dimensional (1D) approaches leading to a hyperbolic system of equations with source terms. Their numerical discretization, associated to the spatial variation of mechanical and geometrical properties, requires advanced numerical solvers that ensure both stability and an accurate description of the dynamics of the system. In this work, we present RoePINNs, a hybrid framework for the embedding of advanced Computational Fluid Dynamics (CFD) solvers into Physics-Informed Neural Networks (PINNs), and give examples of application to Burgers’ equation as well as the propagation of nonlinear waves in elastic arteries, both under the presence of geometric-type source terms, for forward and inverse problems. We demonstrate that Augmented Riemann solvers can be incorporated into the PINN framework with straightforward adjustments to the hyperparameters, providing a promising alternative to automatic differentiation (AD), especially in cases where the solution exhibits strong nonlinearities and physical constraints are required. Benefits of the proposed RoePINN compared with the vanilla PINN based in AD are twofold: on the one hand, this hybrid approach employs numerical differentiation by means of support points in the surroundings of the collocation points, hence the robustness, generalization capacity and tunability of the PINNs are, in most cases, largely enhanced. On the other hand, the RoePINN incorporates the numerical solver, hence it is also capable of capturing sharp discontinuities with an order-of-magnitude improvement in accuracy compared with the vanilla version.
000153155 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2021-128972OA-I00$$9info:eu-repo/grantAgreement/ES/AEI/PID2023-148975OB-I00$$9info:eu-repo/grantAgreement/ES/AEI/PID2023-150074NB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/CNS2023-143599$$9info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031413-I
000153155 540__ $$9info:eu-repo/semantics/closedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000153155 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000153155 700__ $$0(orcid)0000-0003-4130-5866$$aRamírez, J.$$uUniversidad de Zaragoza
000153155 700__ $$0(orcid)0000-0001-8674-1042$$aGarcía-Navarro, P.$$uUniversidad de Zaragoza
000153155 700__ $$0(orcid)0000-0002-1386-5543$$aMurillo, J.$$uUniversidad de Zaragoza
000153155 7102_ $$15001$$2600$$aUniversidad de Zaragoza$$bDpto. Ciencia Tecnol.Mater.Fl.$$cÁrea Mecánica de Fluidos
000153155 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000153155 773__ $$g440 (2025), 117933 [32 pp.]$$pComput. methods appl. mech. eng.$$tComputer Methods in Applied Mechanics and Engineering$$x0045-7825
000153155 8564_ $$s3202626$$uhttps://zaguan.unizar.es/record/153155/files/texto_completo.pdf$$yVersión publicada
000153155 8564_ $$s1909718$$uhttps://zaguan.unizar.es/record/153155/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000153155 909CO $$ooai:zaguan.unizar.es:153155$$particulos$$pdriver
000153155 951__ $$a2025-10-17-14:12:08
000153155 980__ $$aARTICLE