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Journal Articles

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

Pan Du, Jian-Xun Wang
Journal: Journal of Biomechanical Engineering
Publisher: ASME
Article Type: Research-Article
J Biomech Eng. December 2022, 144(12): 121009.
Paper No: BIO-22-1104
https://doi.org/10.1115/1.4055809
Published Online: October 17, 2022
... computational fluid dynamics cardiovascular modeling machine learning uncertainty quantification model inference 31 03 2022 21 09 2022 17 10 2022 1 Corresponding author. e-mail:  jwang33@nd.edu Copyright © 2022 by ASME 2022 0148-0731/2022/144(12)/121009/12/ $25.00 ...
View Article titled, Reducing Geometric Uncertainty in Computational Hemodynamics by Deep Learning-Assisted Parallel-Chain MCMC
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Topics: Algorithms, Aorta, Chain, Computational fluid dynamics, Flow (Dynamics), Hemodynamics, Modeling, Simulation, Uncertainty, Artificial neural networks
Journal Articles

A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model

Balaji Rengarajan, Sourav S. Patnaik, Ender A. Finol
Journal: Journal of Biomechanical Engineering
Publisher: ASME
Article Type: Research-Article
J Biomech Eng. December 2021, 143(12): 121004.
Paper No: BIO-21-1103
https://doi.org/10.1115/1.4051905
Published Online: September 1, 2021
... resulting from a variable reduction procedure. We compared the performance of the NN models with two standard machine learning algorithms (MARS: multivariate adaptive regression splines and GAM: generalized additive model) and a linear regression model (GLM: generalized linear model). With the reduced sets...
View Article titled, A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model
Open the PDF for A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model in another window
Topics: Algorithms, Aneurysms, Artificial neural networks, Machine learning, Stress, Neural network models, Errors, Finite element analysis, Biomechanics, Goodness-of-fit tests
Includes: Supplementary data