[2604.03197] Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis

Computer Science > Machine Learning arXiv:2604.03197 (cs) [Submitted on 3 Apr 2026] Title:Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis Authors:Sokratis J. Anagnostopoulos, George Rovas, Vasiliki Bikia, Theodore G. Papaioannou, Athanase D. Protogerou, Nikolaos Stergiopulos View a PDF of the paper titled Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis, by Sokratis J. Anagnostopoulos and 4 other authors View PDF HTML (experimental) Abstract:Cardiovascular modeling has rapidly advanced over the past few decades due to the rising needs for health tracking and early detection of cardiovascular diseases. While 1-D arterial models offer an attractive compromise between computational efficiency and solution fidelity, their application on large populations or for generating large \emph{in silico} cohorts remains challenging. Certain hemodynamic parameters like the terminal resistance/compliance, are difficult to clinically estimate and often yield non-physiological hemodynamics when sampled naively, resulting in large portions of simulated datasets to be discarded. In this work, we present a systematic framework for training machine learning (ML) models, capable of instantaneous hemodynamic prediction and parameter estimation. We initially start with generating a parametric virtual cohort of patients which is based on the multivariate correlations observed in the large Asklepios clinical dat...