Aortic dissection progresses via delamination of the medial layer of the wall. Notwithstanding the complexity of this process, insight has been gleaned by studying in vitro and in silico the progression of dissection driven by quasi-static pressurization of the intramural space by fluid injection, which demonstrates that the differential propensity of dissection can be affected by spatial distributions of structurally significant interlamellar struts that connect adjacent elastic lamellae. In particular, diverse histological microstructures may lead to differential mechanical behavior during dissection, including the pressure--volume relationship of the injected fluid and the displacement field between adjacent lamellae. In this study, we develop a data-driven surrogate model for the delamination process for differential strut distributions using DeepONet, a new operator--regression neural network. The surrogate model is trained to predict the pressure--volume curve of the injected fluid and the damage progression field of the wall given a spatial distribution of struts, with in silico data generated with a phase-field finite element model. The results show that DeepONet can provide accurate predictions for diverse strut distributions, indicating that this composite branch-trunk neural network can effectively extract the underlying functional relationship between distinctive microstructures and their mechanical properties. More broadly, DeepONet can facilitate surrogate model-based analyses to quantify biological variability, improve inverse design, and predict mechanical properties based on multi-modality experimental data.
翻译:尽管这一过程的复杂性,但通过在体外和硅体内空间的准静态加压,通过注入液体,对内部空间的分解过程进行分解过程的演进进行了研究,这表明分解的不同倾向可能受到结构上重要的隔热层的空间分布的影响,这种分布将相邻的弹性软骨合体连接起来。特别是,不同的生理学微观结构可能导致分解过程中机械行为的差异,包括注射液的压力-体积关系和相邻的瘸子体间移动的机械性能场。在这项研究中,我们为分解过程的分解过程开发了一个数据驱动的代孕模型,利用DeepONet,一个新的操作者-反向神经网络进行这种分流,对投放液的模型进行空间分布,对墙上破坏的机械性变化场进行预测,通过在深度内流体间生成的分流体间压力-量关系和机械性变异性变异性变异性变异性,在深度内生成的投影性数据,在深层网络内部的分流分析中有效进行。