biostudies-literatureRyan WAdditional VenturesEngineering and Physical Sciences Research CouncilNational Science Foundatione70147https://www.ebi.ac.uk/biostudies/studies/S-EPMC12905477biostudies-literatureUnknown42(2)There are many computational models set up to predict blood flow and pressure in vascular networks. Methods for a single forward solution of such models are well established but become problematic in clinical applications, where model calibration and patient-specific parameter estimation call for repeated forward simulations of the model requiring substantial computational costs. A potential workaround is emulation, which approximates the original mathematical model by a statistical or machine learning surrogate model. Our methodological framework is based on physics-informed neural networks, with a particular focus on patient-specific model calibration. Once fully trained, our machine learning model predicts flow and pressure waveforms in a fraction of the time required by the numerical sInternational journal for numerical methods in biomedical engineeringPhysics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification.PMC12905477DGE-21371001449780DMS-22314822342344EP/T017899/1Vyshemirskiy VHusmeier DOlufsen MTaylor-LaPole ARyan WfalsePhysics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification.There are many computational models set up to predict blood flow and pressure in vascular networks. Methods for a single forward solution of such models are well established but become problematic in clinical applications, where model calibration and patient-specific parameter estimation call for repeated forward simulations of the model requiring substantial computational costs. A potential workaround is emulation, which approximates the original mathematical model by a statistical or machine learning surrogate model. Our methodological framework is based on physics-informed neural networks, with a particular focus on patient-specific model calibration. Once fully trained, our machine learning model predicts flow and pressure waveforms in a fraction of the time required by the numerical s2026-01-01T00:00:00Z2026 Feb2026-07-15T21:25:10.529Z2026-07-09T10:14:22.509ZS-EPMC129054774168920410.1002/cnm.70147