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Found 8 papers, 0 papers with code
Dimension-reduced KRnet maps for high-dimensional Bayesian inverse problems
We present a dimension-reduced KRnet map approach (DR-KRnet) for high-dimensional Bayesian inverse problems, which is based on an explicit construction of a map that pushes forward the prior measure to the posterior measure in the latent space.
Streaming data recovery via Bayesian tensor train decomposition
Drawing on the streaming variational Bayes method, we introduce the TT format into Bayesian tensor decomposition methods for streaming data, and formulate posteriors of TT cores.
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems
To address these challenges, we propose a novel approximation method for estimating the high-dimensional posterior distribution.
A domain-decomposed VAE method for Bayesian inverse problems
Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs).
Deep neural network based adaptive learning for switched systems
Currently, deep neural network based methods are actively developed for learning governing equations in unknown dynamic systems, but their efficiency can degenerate for switching systems, where structural changes exist at discrete time instants.
Adaptive deep density approximation for Fokker-Planck equations
In this paper we present an adaptive deep density approximation strategy based on KRnet (ADDA-KR) for solving the steady-state Fokker-Planck (F-P) equations.
Tensor Train Random Projection
This work proposes a novel tensor train random projection (TTRP) method for dimension reduction, where pairwise distances can be approximately preserved.
D3M: A deep domain decomposition method for partial differential equations
A state-of-the-art deep domain decomposition method (D3M) based on the variational principle is proposed for partial differential equations (PDEs).
