A MCMC method based on surrogate model and Gaussian process parameterization for infinite Bayesian PDE inversion

DOI10.1016/J.JCP.2024.112970MaRDI QIDQ6553787

Qing-Ping Zhou, Zheng Hu, Hongqiao Wang

Publication date: 11 June 2024

Published in: Journal of Computational Physics (Search for Journal in Brave)

Could not fetch data.

Parametric inference (62Fxx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Probabilistic methods, stochastic differential equations (65Cxx)

Cites Work

Title not available (Why is that?)
Title not available (Why is that?)
Besov priors for Bayesian inverse problems
Numerical analysis of blood flow in the heart
New Cartesian grid methods for interface problems using the finite element formulation
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems
Stein variational gradient descent with local approximations
Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction
Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks
Bayesian calibration of computer models. (With discussion)
Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems
Inverse problems: A Bayesian perspective
Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems
Model Estimations Biased by Truncated Expansions: Possible Artifacts in Seismic Tomography
Inverse problems as statistics
Approximation errors and model reduction with an application in optical diffusion tomography
Handbook of Markov Chain Monte Carlo
Fast Bayesian approach for parameter estimation
Immersed-Interface Finite-Element Methods for Elliptic Interface Problems with Nonhomogeneous Jump Conditions
Posterior consistency for Gaussian process approximations of Bayesian posterior distributions
Parallel Local Approximation MCMC for Expensive Models
Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach
An Introduction to Nonlinear Finite Element Analysis
On the local Lipschitz stability of Bayesian inverse problems
Bayesian Inference and Uncertainty Quantification for Medical Image Reconstruction with Poisson Data
Deep neural network expression of posterior expectations in Bayesian PDE inversion
Machine Learning and Computational Mathematics
An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems
Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems
Sequential Monte Carlo without likelihoods
A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion
Preconditioning Markov Chain Monte Carlo Simulations Using Coarse-Scale Models
Accurate Solution of Bayesian Inverse Uncertainty Quantification Problems Combining Reduced Basis Methods and Reduction Error Models
A posteriori stochastic correction of reduced models in delayed-acceptance MCMC, with application to multiphase subsurface inverse problems

This page was built for publication: A MCMC method based on surrogate model and Gaussian process parameterization for infinite Bayesian PDE inversion

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6553787)