Pages that link to "Item:Q5878248"
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The following pages link to Nonlinear dimension reduction for surrogate modeling using gradient information (Q5878248):
Displaying 11 items.
- Gradient-enhanced surrogate modeling based on proper orthogonal decomposition (Q455872) (← links)
- Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold (Q2020284) (← links)
- Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems (Q5044972) (← links)
- High-Dimensional Nonlinear Multi-Fidelity Model with Gradient-Free Active Subspace Method (Q5162366) (← links)
- Nonlinear Level Set Learning for Function Approximation on Sparse Data with Applications to Parametric Differential Equations (Q5864754) (← links)
- Model Reduction for Nonlinear Systems by Balanced Truncation of State and Gradient Covariance (Q6054284) (← links)
- Conditional Karhunen-Loève regression model with basis adaptation for high-dimensional problems: uncertainty quantification and inverse modeling (Q6118544) (← links)
- Level Set Learning with Pseudoreversible Neural Networks for Nonlinear Dimension Reduction in Function Approximation (Q6155903) (← links)
- Principal feature detection via \(\phi \)-Sobolev inequalities (Q6589581) (← links)
- On the representation and learning of monotone triangular transport maps (Q6659499) (← links)
- A review of recent advances in surrogate models for uncertainty quantification of high-dimensional engineering applications (Q6663327) (← links)
