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
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