Abstract:This work aims to predict blood flow with non-Newtonian viscosity in stenosed arteries using convolutional neural network (CNN) surrogate models.
An alternating Schwarz domain decomposition method is proposed which uses CNN-based subdomain solvers.
A universal subdomain solver (USDS) is trained on a single, fixed geometry and then applied for each subdomain solve in the Schwarz method.
Results for two-dimensional stenotic arteries of varying shape and length for different inflow conditions are presented and statistically evaluated. One key finding, when using a limited amount of training data, is that incorporating a physics-aware constraint, as, in our case, flow rate conservation, into the USDS improves the prediction accuracy and convergence behavior of the Schwarz method compared to a purely data-driven USDS. As the USDS is a data-driven, inexact subdomain solver, admissible parameter ranges for the geometry and inflow configurations must be defined and tested.
From: Axel Klawonn [view email]
[v1]
Fri, 19 Sep 2025 11:56:54 UTC (6,301 KB)
[v2]
Wed, 24 Jun 2026 17:10:58 UTC (2,861 KB)
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