Résumé
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.