Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning
Fig 1
Overview of the proposed approach to perform uncertainty quantification.
We trained a deep neural network using the datasets obtained from one-dimensional–zero-dimensional (1D–0D) simulation. The datasets were generated by randomly sampling 60 inputs (column vector x∈ℝ60) describing the geometry of cerebral arteries and stenoses, and collecting the corresponding 45 simulation outputs (column vector ysim∈ℝ45) of time-averaged flow rates and pressures. After performing the data acquisition and model training in the offline phase, the surrogate model was used in the online phase to predict the outputs rapidly. This ensured a fast and efficient uncertainty quantification.