Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forward model's accuracy

DOI10.1007/s00466-022-02214-6OpenAlexW4292478225MaRDI QIDQ2683299

Phaedon-Stelios Koutsourelakis, Jörg F. Unger, Annika Robens-Radermacher, Thomas Titscher, Isabela Coelho Lima, Daniel Kadoke

Publication date: 10 February 2023

Published in: Computational Mechanics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00466-022-02214-6

zbMATH Keywords

proper generalized decompositionvariational inferencedigital twinBayesian model identificationdistributed damage identificationgoal-oriented methodlog-normal random field

Mathematics Subject Classification ID

Random structure in solid mechanics (74E35) Brittle damage (74R05) Stochastic and other probabilistic methods applied to problems in solid mechanics (74S60)

Related Items (1)

Hierarchically goal-oriented prediction of skeletal muscle tissue constitutive behavior considering histological characteristics

Uses Software

• FEniCS
• GitHub
• ADVI

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Tensor Decompositions and Applications
• Harold Jeffreys's default Bayes factor hypothesis tests: explanation, extension, and application in psychology
• Recent advances and new challenges in the use of the proper generalized decomposition for solving multidimensional models
• A first step towards the use of proper general decomposition method for structural optimization
• A proper generalized decomposition for the solution of elliptic problems in abstract form by using a functional Eckart-Young approach
• A priori model reduction through proper generalized decomposition for solving time-dependent partial differential equations
• Parametric solutions involving geometry: a step towards efficient shape optimization
• A generalized spectral decomposition technique to solve a class of linear stochastic partial differential equations
• Fast model updating coupling Bayesian inference and PGD model reduction
• On the existence of a progressive variational vademecum based on the proper generalized decomposition for a class of elliptic parameterized problems
• An introduction to variational methods for graphical models
• Bayesian inference of random fields represented with the Karhunen-Loève expansion
• The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions
• Generalized spectral decomposition method for solving stochastic finite element equations: invariant subspace problem and dedicated algorithms
• A proper generalized decomposition (PGD) approach to crack propagation in brittle materials: with application to random field material properties
• A literature survey of low-rank tensor approximation techniques
• Model order reduction based on proper generalized decomposition for the propagation of uncertainties in structural dynamics
• Coupling finite element and reliability analysis through proper generalized decomposition model reduction
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Variational Bayesian Inference for a Nonlinear Forward Model
• Inverse Problem Theory and Methods for Model Parameter Estimation
• Bayes Factors
• BEYOND BLACK-BOXES IN BAYESIAN INVERSE PROBLEMS AND MODEL VALIDATION: APPLICATIONS IN SOLID MECHANICS OF ELASTOGRAPHY
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications

This page was built for publication: Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forward model's accuracy