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Found 6 papers, 1 papers with code
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces
Recently, generative models such as Generative Adversarial Networks (GANs) have shown great potential in approximating complex high dimensional conditional distributions and have paved the way for characterizing posterior densities in Bayesian inverse problems, yet the problems' high dimensionality and high nonlinearity often impedes the model's training.
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables.
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations.
Compressive sensing adaptation for polynomial chaos expansions
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ.
Efficient Bayesian experimentation using an expected information gain lower bound
We propose a framework where a lower bound of the expected information gain is used as an alternative design criterion.
Variational Reformulation of Bayesian Inverse Problems
The classical approach to inverse problems is based on the optimization of a misfit function.
