Fast surrogate of 3-D patient-specific computational fluid dynamics using statistical shape modeling and deep Learning

Optimization and uncertainty quantification (UQ) have been playing an increasingly important role in computational hemodynamics. However, existing methods based on principled modeling and classic numerical techniques have faced significant challenges, particularly when it comes to complex 3D patient-specific shapes in the real world. First, it is notoriously difficult to parameterize the input space of arbitrarily complex 3-D geometries. Second, the process often involves massive forward simulations, which are extremely computationally demanding or even infeasible. We propose a novel deep learning solution to address these challenges and enable scalable geometric UQ and optimization. Specifically, a statistical generative model for 3-D patient-specific shapes will be constructed based on a handful of available baseline patient geometries.  An unsupervised shape correspondence solution is used to enable geometric morphing and a compact geometric design space can then be constructed by the statistical generative shape model. In order to build a fast forward map between geometric input space to the solution space of functional information, we propose a supervised deep learning solution, which will facilitate shape optimization and UQ analysis in a massively scalable manner.