VAE-KRnet and Its Applications to Variational Bayes

DOI10.4208/cicp.OA-2021-0087zbMath1486.62096arXiv2006.16431OpenAlexW4220785892MaRDI QIDQ5077693

Xiaoliang Wan, Shuangqing Wei

Publication date: 19 May 2022

Published in: Communications in Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.16431

zbMATH Keywords

variational Bayesuncertainty quantificationBayesian inverse problemsdeep learninggenerative modeling

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Density estimation (62G07) Artificial neural networks and deep learning (68T07) Bayesian problems; characterization of Bayes procedures (62C10)

Related Items (4)

On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements ⋮ Physics-informed neural networks for approximating dynamic (hyperbolic) PDEs of second order in time: error analysis and algorithms ⋮ DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations ⋮ Numerical computation of partial differential equations by hidden-layer concatenated extreme learning machine

Uses Software

• Adam
• NICE
• Glow
• torchdiffeq
• ANODEs
• Flow-GAN
• Wasserstein GAN

Cites Work

• Unnamed Item
• Hybrid Monte Carlo on Hilbert spaces
• Coupling the reduced-order model and the generative model for an importance sampling estimator
• Adaptive deep density approximation for Fokker-Planck equations
• Multilevel Monte Carlo Methods
• From Knothe's Transport to Brenier's Map and a Continuation Method for Optimal Transport
• Multivariate Density Estimation

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