标题： 使用神经算子的血小板变形高保真描述 显示英文标题

标题： High-Fidelity Description of Platelet Deformation Using a Neural Operator

摘要： 本文工作的目标是研究神经算子（DeepONet）准确捕捉血小板膜在剪切流中复杂变形的能力。由神经算子近似的代理模型根据初始构象和血液流动施加的剪切应力来预测变形后的膜构象。训练数据集来源于在LAMMPS中实现的粒子动力学模拟。神经算子以约0.5%的模式误差分布捕获膜粒子的动力学。所提出的实现方法可作为将亚血小板动力学整合到血栓形成的多尺度计算模型中的可扩展方法。 显示英文摘要

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.