Work

Reducing Geometric Uncertainty in Computational Hemodynamics by Deep Learning-Assisted Parallel-Chain MCMC

Year: 2022

Type: article

Abstract: Computational hemodynamic modeling has been widely used in cardiovascular research and healthcare. However, the reliability of model predictions is largely dependent on the uncertainties of modeling parameters and boundary conditions, which should be carefully quantified and further reduced with available measurements. In this work, we focus on propagating and reducing the uncertainty of vascular geometries within a Bayesian framework. A novel deep learning (DL)-assisted parallel Markov chain Monte Carlo (MCMC) method is presented to enable efficient Bayesian posterior sampling and geometric uncertainty reduction. A DL model is built to approximate the geometry-to-hemodynamic map, which is trained actively using online data collected from parallel MCMC chains and utilized for early rejection of unlikely proposals to facilitate convergence with less expensive full-order model evaluations. Numerical studies on two-dimensional aortic flows are conducted to demonstrate the effectiveness and merit of the proposed method.

Source: Journal of Biomechanical Engineering

DOI: 10.1115/1.4055809

Authors: Pan Du, Jian-Xun Wang

Institution: University of Notre Dame

Language: English

Topic: Probabilistic and Robust Engineering Design

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funders: National Science Foundation, Air Force Office of Scientific Research

Awards: G1222367328, CAREER: Forward and Inverse Uncertainty Quantification of Cardiovascular Fluid-Structure Dynamics via Multi-fidelity Physics-Informed Bayesian Geometric Deep Learning