{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ryan W"],"funding":["Additional Ventures","Engineering and Physical Sciences Research Council","National Science Foundation"],"pagination":["e70147"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12905477"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["42(2)"],"pubmed_abstract":["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 s"],"journal":["International journal for numerical methods in biomedical engineering"],"pubmed_title":["Physics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification."],"pmcid":["PMC12905477"],"funding_grant_id":["DGE-2137100","1449780","DMS-2231482","2342344","EP/T017899/1"],"pubmed_authors":["Vyshemirskiy V","Husmeier D","Olufsen M","Taylor-LaPole A","Ryan W"],"additional_accession":[]},"is_claimable":false,"name":"Physics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification.","description":"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 s","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Feb","modification":"2026-07-15T21:25:10.529Z","creation":"2026-07-09T10:14:22.509Z"},"accession":"S-EPMC12905477","cross_references":{"pubmed":["41689204"],"doi":["10.1002/cnm.70147"]}}