High-Fidelity Description of Platelet Deformation Using a Neural Operator

作     者：Laudato, Marco Manzari, Luca Shukla, Khemraj 

作者机构：FLOW Research Center Department of Engineering Mechanics KTH Royal Institute of Technology StockholmSE-10044 Sweden PDC Center for High Performance Computing KTH Royal Institute of Technology StockholmSE-11428 Sweden Division of Applied Mathematics Brown University ProvidenceRI02906 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Shear flow 

摘      要：The goal of this work is to investigate the capability of a neural operator (DeepONet) to accurately capture the complex deformation of a platelet’s membrane under shear flow. The surrogate model approximated by the neural operator predicts the deformed membrane configuration based on its initial configuration and the shear stress exerted by the blood flow. The training dataset is derived from particle dynamics simulations implemented in LAMMPS. The neural operator captures the dynamics of the membrane particles with a mode error distribution of approximately 0.5%. The proposed implementation serves as a scalable approach to integrate sub-platelet dynamics into multi-scale computational models of thrombosis. © 2024, CC BY.