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.

中文翻译：

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使用算子回归神经网络模拟导致主动脉夹层的渐进性壁内损伤

主动脉夹层通过壁中间层的分层进行。尽管这个过程很复杂，但通过在体外和计算机上研究由流体注射对壁内空间的准静态加压驱动的解剖进展，已经收集到了洞察力，这表明解剖的差异倾向可能受空间分布的影响连接相邻弹性薄片的结构上重要的层间支柱。特别是，不同的组织学微观结构可能会导致解剖过程中不同的机械行为，包括注入流体的压力-体积关系和相邻薄片之间的位移场。在这项研究中，我们使用 DeepONet（一种新的算子回归神经网络）为差分支柱分布的分层过程开发了一个数据驱动的替代模型。代理模型经过训练以预测注入流体的压力 - 体积曲线和壁的损伤进展场，给定支柱的空间分布，并使用由相场有限元模型生成的计算机数据。结果表明，DeepONet 可以为不同的支柱分布提供准确的预测，表明这种复合分支 - 树干神经网络可以有效地提取独特的微观结构与其力学性能之间的潜在功能关系。更广泛地说，DeepONet 可以促进基于替代模型的分析，以量化生物变异性，改进逆向设计，