A Flow-rate-conserving CNN-based Domain Decomposition Method for Blood Flow Simulations
This addresses blood flow simulation for medical applications, but it is incremental as it builds on existing domain decomposition and CNN methods with a specific physics-aware enhancement.
The paper tackled predicting blood flow with non-Newtonian viscosity in stenosed arteries using CNN surrogate models, proposing an alternating Schwarz domain decomposition method with a universal subdomain solver trained on fixed geometry; results showed that a physics-aware approach preserving flow rate conservation outperformed purely data-driven methods, improving subdomain solutions and convergence reliability.
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 the need to implement a USDS which preserves some of the physics, as, in our case, flow rate conservation. A physics-aware approach outperforms purely data-driven USDS, delivering improved subdomain solutions and preventing overshooting or undershooting of the global solution during the Schwarz iterations, thereby leading to more reliable convergence.