Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics. (arXiv:2204.08939v1 [physics.med-ph])

Optimization and uncertainty quantification 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 challenging 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 surrogate …

3-d arxiv computational deep learning dynamics fluid dynamics learning patient physics