Fast simulation of coronary in‐stent restenosis: A non‐intrusive data‐driven reduced order surrogate model

• Published in: Proceedings in applied mathematics and mechanics Vol. 24; no. 4
• Main Authors: Shi, Jianye, Manjunatha, Kiran, Reese, Stefanie
• Format: Journal Article
• Language: English
• Published: 01.12.2024
• Subjects: convolutional autoencoder; data‐driven modeling; in‐stent restenosis; reduced order modeling; surrogate model
• ISSN: 1617-7061; 1617-7061
• DOI: 10.1002/pamm.202400067

Modeling and simulation of coronary artery disease (CAD) is of great importance for supporting and predicting the outcome of percutaneous coronary intervention (PCI). However, an in silico model generally requires heavy computational resources. An effective reduced order surrogate model is indispensable in this context. This study aims to develop a non‐intrusive data‐driven reduced order surrogate model for coronary in‐stent restenosis (ISR) incorporating anti‐inflammatory drugs embedded in the drug‐eluting stents. The constitutive model includes a detailed multiphysics approach based on partial differential equations (PDEs), which include descriptions of platelet aggregation, growth‐factor release, cellular motility and drug deposition. Dimensionality reduction is carried out based on a 3D convolutional autoencoder, which comprises an encoder and decoder. The former condenses the full‐order solution into a lower‐dimensional latent space, while the latter recovers the full solution from the latent space. Special attention is paid to handle the multidimensional outputs and network architecture.