Solving Bayesian inverse problems from the perspective of deep generative networks
Sign Up to like & getrecommendations! Published in 2019 at "Computational Mechanics"
DOI: 10.1007/s00466-019-01739-7
Abstract: Deep generative networks have achieved great success in high dimensional density approximation, especially for applications in natural images and language. In this paper, we investigate their approximation capability in capturing the posterior distribution in Bayesian… read more here.
Keywords: bayesian inverse; deep generative; inverse problems; solving bayesian ... See more keywords
Unbiased parameter estimation for bayesian inverse problems
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DOI: 10.1007/s11222-025-10768-7
Abstract: In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a… read more here.
Keywords: methodology; inverse problems; bayesian inverse; parameter estimation ... See more keywords
Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data
Sign Up to like & getrecommendations! Published in 2022 at "Annals of Nuclear Energy"
DOI: 10.1016/j.anucene.2021.108782
Abstract: Additive manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas due to its ability to rapidly produce, prototype, and customize designs. AM techniques afford significant opportunities in regard to nuclear… read more here.
Keywords: uncertainty; pool model; melt pool; bayesian inverse ... See more keywords
Γ -convergence of Onsager–Machlup functionals: I. With applications to maximum a posteriori estimation in Bayesian inverse problems
Sign Up to like & getrecommendations! Published in 2021 at "Inverse Problems"
DOI: 10.1088/1361-6420/ac3f81
Abstract: The Bayesian solution to a statistical inverse problem can be summarised by a mode of the posterior distribution, i.e. a maximum a posteriori (MAP) estimator. The MAP estimator essentially coincides with the (regularised) variational solution… read more here.
Keywords: onsager machlup; bayesian inverse; problem; maximum posteriori ... See more keywords
Sequential Kalman tuning of the t-preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems
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DOI: 10.1088/1361-6420/ad934b
Abstract: Ensemble Kalman Inversion (EKI) has been proposed as an efficient method for the approximate solution of Bayesian inverse problems with expensive forward models. However, when applied to the Bayesian inverse problem EKI is only exact… read more here.
Keywords: preconditioned crank; kalman; inverse problems; inverse ... See more keywords
ANOVA-GP Modeling for High-Dimensional Bayesian Inverse Problems
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DOI: 10.3390/math12020301
Abstract: Markov chain Monte Carlo (MCMC) stands out as an effective method for tackling Bayesian inverse problems. However, when dealing with computationally expensive forward models and high-dimensional parameter spaces, the challenge of repeated sampling becomes pronounced.… read more here.
Keywords: inverse problems; gaussian process; dimensional bayesian; high dimensional ... See more keywords