Adaptive operator learning for infinite-dimensional Bayesian inverse problems (Q6669407)

scientific article; zbMATH DE number 7973170

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English	Adaptive operator learning for infinite-dimensional Bayesian inverse problems	scientific article; zbMATH DE number 7973170

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instance of

scholarly article

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title

Adaptive operator learning for infinite-dimensional Bayesian inverse problems (English)

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SIAM/ASA Journal on Uncertainty Quantification

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publication date

22 January 2025

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zbMATH Keywords

operator learning

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deeponet

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Bayesian inverse problems

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unscented Kalman inversion

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MaRDI profile type

Publication

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cites work

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

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A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems

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A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion

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A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic Newton MCMC with Application to Ice Sheet Flow Inverse Problems

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Data-driven model reduction for the Bayesian solution of inverse problems

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Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems

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On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems

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Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems

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Stochastic spectral methods for efficient Bayesian solution of inverse problems

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Convergence analysis of surrogate-based methods for Bayesian inverse problems

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An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems

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Solving high-dimensional partial differential equations using deep learning

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ

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Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

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Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

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An Acceleration Strategy for Randomize-Then-Optimize Sampling Via Deep Neural Networks

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Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks

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When and why PINNs fail to train: a neural tangent kernel perspective

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Failure-Informed Adaptive Sampling for PINNs

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Failure-informed adaptive sampling for PINNs. II: Combining with re-sampling and subset simulation

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Residual-based error correction for neural operator accelerated Infinite-dimensional Bayesian inverse problems

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Calibrate, emulate, sample

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Stein variational gradient descent with local approximations

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Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler

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Ensemble Kalman methods for inverse problems

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Iterated Kalman methodology for inverse problems

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Inverse problems: A Bayesian perspective

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Handbook of Markov Chain Monte Carlo

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Error estimates for DeepONets: a deep learning framework in infinite dimensions

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Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems

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Convergence acceleration of ensemble Kalman inversion in nonlinear settings

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Consensus‐based sampling

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Analysis of the Ensemble and Polynomial Chaos Kalman Filters in Bayesian Inverse Problems

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Efficient derivative-free Bayesian inference for large-scale inverse problems

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Adaptive Ensemble Kalman Inversion with Statistical Linearization

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Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion

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AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS

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Identifiers

DOI

10.1137/24m1643815

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6669407