Bi-fidelity variational auto-encoder for uncertainty quantification (Q6202982)

scientific article; zbMATH DE number 7824889

Language	Label	Description	Also known as
English	Bi-fidelity variational auto-encoder for uncertainty quantification	scientific article; zbMATH DE number 7824889

Statements

instance of

scholarly article

0 references

title

Bi-fidelity variational auto-encoder for uncertainty quantification (English)

0 references

0 references

0 references

0 references

0 references

0 references

Computer Methods in Applied Mechanics and Engineering

0 references

publication date

26 March 2024

0 references

full work available at URL

https://arxiv.org/abs/2305.16530

0 references

zbMATH Keywords

uncertainty quantification

0 references

variational auto-encoder

0 references

multi-fidelity

0 references

generative modeling

0 references

transfer learning

0 references

MaRDI profile type

MaRDI publication profile

0 references

cites work

A weighted \(\ell_1\)-minimization approach for sparse polynomial chaos expansions

0 references

Compressive sampling of polynomial chaos expansions: convergence analysis and sampling strategies

0 references

Q3394879

0 references

Multi-output local Gaussian process regression: applications to uncertainty quantification

0 references

Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

0 references

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

0 references

Solving inverse problems using conditional invertible neural networks

0 references

Certified Reduced Basis Methods for Parametrized Partial Differential Equations

0 references

KERNEL OPTIMIZATION FOR LOW-RANK MULTIFIDELITY ALGORITHMS

0 references

Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

0 references

Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

0 references

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

0 references

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

0 references

Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

0 references

A generalized approximate control variate framework for multifidelity uncertainty quantification

0 references

A low-rank control variate for multilevel Monte Carlo simulation of high-dimensional uncertain systems

0 references

Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction

0 references

Multi-fidelity non-intrusive polynomial chaos based on regression

0 references

Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems

0 references

Predicting the output from a complex computer code when fast approximations are available

0 references

Accurate Uncertainty Quantification Using Inaccurate Computational Models

0 references

A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations

0 references

Bayesian Calibration of Computer Models

0 references

Q5871909

0 references

Q5405143

0 references

ON TRANSFER LEARNING OF NEURAL NETWORKS USING BI-FIDELITY DATA FOR UNCERTAINTY PROPAGATION

0 references

Neural network training using \(\ell_1\)-regularization and bi-fidelity data

0 references

Kolmogorov widths and low-rank approximations of parametric elliptic PDEs

0 references

Coherence motivated sampling and convergence analysis of least squares polynomial chaos regression

0 references

Basis adaptive sample efficient polynomial chaos (BASE-PC)

0 references

Identifiers

arXiv ID

2305.16530

0 references

Mathematics Subject Classification ID

0 references

0 references

0 references

0 references

10.1016/J.CMA.2024.116793

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:6202982