A VRNN-DIRT framework with tensor trains delivers low-variance failure probability estimates for 3D heterogeneous composites in dimensions up to 150.
Survey of multifidelity methods in uncertainty propagation, inference, and optimization.SIAM Review, 60(3):550–591, 2018
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
An ensemble of hierarchical kriging emulators aggregated by Bayesian model averaging yields accurate multi-fidelity predictions with uncertainty-driven adaptive sampling that outperforms single models on benchmarks.
citing papers explorer
-
Multiscale Structural Reliability Analysis in high dimensions with Tensor Trains and Physics-Augmented Neural Networks
A VRNN-DIRT framework with tensor trains delivers low-variance failure probability estimates for 3D heterogeneous composites in dimensions up to 150.
-
An ensemble-based approach for multi-fidelity emulation and adaptive sampling
An ensemble of hierarchical kriging emulators aggregated by Bayesian model averaging yields accurate multi-fidelity predictions with uncertainty-driven adaptive sampling that outperforms single models on benchmarks.