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.

The authors acknowledge financial support provided by the Ministerio de Ciencia, Innovación y Universidades (MCIN/ AEI/10.13039/501100011033, MICIU/AEI/10.13039/501100011033/ FEDER, UE) through grants PID2022-141957OB-C21, PID2023-153082OB-I00 and CEX2018-000797-S; the Fondazione Regionale per la Ricerca Biomedica (Regione Lombardia), via project ID 3432721; and the Italian Ministry of University and Research (PRIN 2022, grant 2022ZKEP8S). The authors acknowledge Zhongzhao Teng of the University of Cambridge for providing the realistic 2D geometries used in this work. Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

Peer Reviewed

Postprint (published version)