Pages that link to "Item:Q2002273"
From MaRDI portal
The following pages link to Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (Q2002273):
Displaying 14 items.
- Learning to solve Bayesian inverse problems: an amortized variational inference approach using Gaussian and flow guides (Q6560691) (← links)
- Wave-packet behaviors of the defocusing nonlinear Schrödinger equation based on the modified physics-informed neural networks (Q6562238) (← links)
- TDOR-MPINNs: multi-output physics-informed neural networks based on time differential order reduction for solving coupled Klein-Gordon-Zakharov systems (Q6568897) (← links)
- Random field of homogeneous and multi-material structures by the smoothed finite element method and Karhunen-Loève expansion (Q6578060) (← links)
- Operator learning using random features: a tool for scientific computing (Q6585281) (← links)
- Neural and spectral operator surrogates: unified construction and expression rate bounds (Q6601288) (← links)
- Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification (Q6639348) (← links)
- Accurate data-driven surrogates of dynamical systems for forward propagation of uncertainty (Q6648584) (← links)
- Sparse-grid sampling recovery and numerical integration of functions having mixed smoothness (Q6650705) (← links)
- Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators (Q6663284) (← links)
- Bayesian neural networks for predicting uncertainty in full-field material response (Q6663292) (← links)
- A review of recent advances in surrogate models for uncertainty quantification of high-dimensional engineering applications (Q6663327) (← links)
- Adaptive operator learning for infinite-dimensional Bayesian inverse problems (Q6669407) (← links)
- From obstacle problems to neural insights: feedforward neural network modeling of ice thickness (Q6670343) (← links)
