Adaptive reduced order modeling to assess peak stresses in heterogeneous arterial sections

Abstract We introduce an adaptive model reduction approach to compute peak Von Mises stress (pVMS) in heterogeneous arterial sections. The pipeline follows a standard two-phase process: first, we construct the training set of displacement snapshots obtained from the full order model offline, and then we compute pVMS in the online phase. We adaptively enrich the modal representation in critical regions (around the lumen and in calcified areas) using a level-set approach. Optimized for efficient pVMS computation, as a key plaque vulnerability indicator, this technique significantly reduces the computational cost of training machine learning models to classify plaque vulnerability based on pVMS.