Pages that link to "Item:Q2083198"
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The following pages link to A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations (Q2083198):
Displaying 12 items.
- Machine-learning-based modeling of coarse-scale error, with application to uncertainty quantification (Q1787655) (← links)
- Multi-fidelity uncertainty quantification method with application to nonlinear structural response analysis (Q1985164) (← links)
- A generalized multi-fidelity simulation method using sparse polynomial chaos expansion (Q2033075) (← links)
- A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems (Q2138808) (← links)
- Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (Q2179219) (← links)
- Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics (Q2184337) (← links)
- Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian processes (Q2246340) (← links)
- A fast multi-fidelity method with uncertainty quantification for complex data correlations: application to vortex-induced vibrations of marine risers (Q2246346) (← links)
- Residual Gaussian process: a tractable nonparametric Bayesian emulator for multi-fidelity simulations (Q2247250) (← links)
- Proper orthogonal decompositions in multifidelity uncertainty quantification of complex simulation models (Q2875310) (← links)
- Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis (Q6187654) (← links)
- Bi-fidelity variational auto-encoder for uncertainty quantification (Q6202982) (← links)
