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Aortic dissection progresses via delamination of the medial layer of thewall. Notwithstanding the complexity of this process, insight has been gleanedby studying in vitro and in silico the progression of dissection driven byquasi-static pressurization of the intramural space by fluid injection, whichdemonstrates that the differential propensity of dissection can be affected byspatial distributions of structurally significant interlamellar struts thatconnect adjacent elastic lamellae. In particular, diverse histologicalmicrostructures may lead to differential mechanical behavior during dissection,including the pressure--volume relationship of the injected fluid and thedisplacement field between adjacent lamellae. In this study, we develop adata-driven surrogate model for the delamination process for differential strutdistributions using DeepONet, a new operator--regression neural network. Thesurrogate model is trained to predict the pressure--volume curve of theinjected fluid and the damage progression field of the wall given a spatialdistribution of struts, with in silico data generated with a phase-field finiteelement model. The results show that DeepONet can provide accurate predictionsfor diverse strut distributions, indicating that this composite branch-trunkneural network can effectively extract the underlying functional relationshipbetween distinctive microstructures and their mechanical properties. Morebroadly, DeepONet can facilitate surrogate model-based analyses to quantifybiological variability, improve inverse design, and predict mechanicalproperties based on multi-modality experimental data.

Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network