Weak neural variational inference for solving Bayesian inverse problems \textit{without} forward models: applications in elastography
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Publication:6663307
DOI10.1016/j.cma.2024.117493MaRDI QIDQ6663307
Phaedon-Stelios Koutsourelakis, Yaohua Zang, Vincent C. Scholz
Publication date: 14 January 2025
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Cites Work
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- Multi-output separable Gaussian process: towards an efficient, fully Bayesian paradigm for uncertainty quantification
- Diffusion limits of the random walk Metropolis algorithm in high dimensions
- Automated solution of differential equations by the finite element method. The FEniCS book
- Weak adversarial networks for high-dimensional partial differential equations
- ANOVA Gaussian process modeling for high-dimensional stochastic computational models
- A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters
- Stochastic spectral methods for efficient Bayesian solution of inverse problems
- Exponential convergence of Langevin distributions and their discrete approximations
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- Gaussian Markov random field priors for inverse problems
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
- Solving inverse problems using conditional invertible neural networks
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables
- Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
- Variational Bayesian approximation of inverse problems using sparse precision matrices
- Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Sparse variational Bayesian approximations for nonlinear inverse problems: applications in nonlinear elastography
- Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems
- A novel Bayesian strategy for the identification of spatially varying material properties and model validation: an application to static elastography
- Unique identifiability of elastic parameters from time-dependent interior displacement measurement
- Elastic modulus imaging: on the uniqueness and nonuniqueness of the elastography inverse problem in two dimensions
- Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
- Inverse Problem Theory and Methods for Model Parameter Estimation
- BEYOND BLACK-BOXES IN BAYESIAN INVERSE PROBLEMS AND MODEL VALIDATION: APPLICATIONS IN SOLID MECHANICS OF ELASTOGRAPHY
- Numerical solution of inverse problems by weak adversarial networks
- Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective
- Bayesian computation: a summary of the current state, and samples backwards and forwards
- Fully probabilistic deep models for forward and inverse problems in parametric PDEs
- Spectral neural operators
- Semi-supervised invertible neural operators for Bayesian inverse problems
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