Bi-fidelity variational auto-encoder for uncertainty quantification
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Publication:6202982
DOI10.1016/j.cma.2024.116793arXiv2305.16530OpenAlexW4391797872MaRDI QIDQ6202982
Alireza Doostan, Stephen R. Becker, Subhayan De, Osman Asif Malik, Nuojin Cheng
Publication date: 26 March 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2305.16530
uncertainty quantificationtransfer learningmulti-fidelitygenerative modelingvariational auto-encoder
Cites Work
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- A weighted \(\ell_1\)-minimization approach for sparse polynomial chaos expansions
- Compressive sampling of polynomial chaos expansions: convergence analysis and sampling strategies
- Multi-output local Gaussian process regression: applications to uncertainty quantification
- Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction
- A low-rank control variate for multilevel Monte Carlo simulation of high-dimensional uncertain systems
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- Coherence motivated sampling and convergence analysis of least squares polynomial chaos regression
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
- A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations
- Solving inverse problems using conditional invertible neural networks
- A generalized approximate control variate framework for multifidelity uncertainty quantification
- A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
- Neural network training using \(\ell_1\)-regularization and bi-fidelity data
- Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
- Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
- Multi-fidelity non-intrusive polynomial chaos based on regression
- Basis adaptive sample efficient polynomial chaos (BASE-PC)
- Bayesian Calibration of Computer Models
- Certified Reduced Basis Methods for Parametrized Partial Differential Equations
- Kolmogorov widths and low-rank approximations of parametric elliptic PDEs
- Accurate Uncertainty Quantification Using Inaccurate Computational Models
- Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
- Predicting the output from a complex computer code when fast approximations are available
- ON TRANSFER LEARNING OF NEURAL NETWORKS USING BI-FIDELITY DATA FOR UNCERTAINTY PROPAGATION
- KERNEL OPTIMIZATION FOR LOW-RANK MULTIFIDELITY ALGORITHMS
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