Stochastic modeling of inhomogeneities in the aortic wall and uncertainty quantification using a Bayesian encoder-decoder surrogate

DOI10.1016/j.cma.2022.115594OpenAlexW4302763982WikidataQ115734532 ScholiaQ115734532MaRDI QIDQ2096832

Malte Rolf-Pissarczyk, Sascha Ranftl, Wolfgang von der Linden, Antonio Pepe, Gloria Wolkerstorfer, Jan Egger, Gerhard A. Holzapfel

Publication date: 11 November 2022

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2202.10244

zbMATH Keywords

finite element analysisaortic dissectionfibrous tissueBayesian uncertainty quantificationbeta random fieldstochastic constitutive modeling

Mathematics Subject Classification ID

Random fields (60G60) Bayesian inference (62F15) Biomechanical solid mechanics (74L15) Mathematical modeling or simulation for problems pertaining to mechanics of deformable solids (74-10)

Related Items (1)

Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis

Uses Software

• FEAP
• darch
• Krylstat
• PyTorch
• ImageNet
• Adam
• Tensor2Tensor
• U-Net
• nnU-Net
• ViT

Cites Work

• Fast Sampling of Gaussian Markov Random Fields
• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• U-Net
• A probabilistic model for bounded elasticity tensor random fields with application to polycrystalline microstructures
• Iterative numerical methods for sampling from high dimensional Gaussian distributions
• Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
• Bayesian learning for neural networks
• Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
• Advances in Gaussian random field generation: a review
• A discrete approach for modeling degraded elastic fibers in aortic dissection
• PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
• A random field model for anisotropic strain energy functions and its application for uncertainty quantification in vascular mechanics
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Stochastic multiscale homogenization analysis of heterogeneous materials under finite deformations with full uncertainty in the microstructure
• Comparative analysis of multiscale Gaussian random field simulation algorithms
• Preconditioned Krylov Subspace Methods for Sampling Multivariate Gaussian Distributions
• Handbook of Uncertainty Quantification
• Handbook of Monte Carlo Methods
• A general polyconvex strain-energy function for fiber-reinforced materials
• Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy
• Probability Theory
• Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
• Bayesian Probability Theory
• Random Fields
• High-Order Collocation Methods for Differential Equations with Random Inputs
• A Fast Learning Algorithm for Deep Belief Nets
• A new constitutive framework for arterial wall mechanics and a comparative study of material models
• Random forests
• Mechanics of random and multiscale microstructures
• Nested sampling for general Bayesian computation
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Stochastic modeling of inhomogeneities in the aortic wall and uncertainty quantification using a Bayesian encoder-decoder surrogate