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RISReducing Geometric Uncertainty in Computational Hemodynamics by Deep LearningAssisted ParallelChain MCMC
Publisher: The American Society of Mechanical Engineers (ASME)
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 ...
Propagation of Input Uncertainty in Presence of Model-Form Uncertainty: A Multifidelity Approach for Computational Fluid Dynamics Applications
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Proper quantification and propagation of uncertainties in computational simulations are of critical importance. This issue is especially challenging for computational fluid dynamics (CFD) applications. A particular obstacle ...
Physics-Informed Bayesian Neural Networks for Solving Phonon Boltzmann Transport Equation in Forward and Inverse Problems With Sparse and Noisy Data
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Nondiffusive phonon transport presents significant challenges in micro/nanoscale thermal characterization, compounded by the limitations of experimental-numerical techniques and the presence of measurement noise. Additionally, ...