Physics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification.

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

SUBMITTER: Ryan W 

PROVIDER: S-EPMC12905477 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature