Pages that link to "Item:Q2214527"
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The following pages link to Compressive sensing adaptation for polynomial chaos expansions (Q2214527):
Displaying 17 items.
- Basis adaptation in homogeneous chaos spaces (Q348704) (← links)
- Sparse polynomial chaos expansions using variational relevance vector machines (Q781971) (← links)
- An ensemble synthetic eddy method for accurate treatment of inhomogeneous turbulence (Q2102020) (← links)
- Novel algorithm for flexible multibody systems with hybrid uncertainties (Q2110864) (← links)
- Solution of the 3D density-driven groundwater flow problem with uncertain porosity and permeability (Q2176414) (← links)
- PLS-based adaptation for efficient PCE representation in high dimensions (Q2220565) (← links)
- Accelerated basis adaptation in homogeneous chaos spaces (Q2246311) (← links)
- Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian processes (Q2246340) (← links)
- A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
- On the influence of over-parameterization in manifold based surrogates and deep neural operators (Q2687573) (← links)
- Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark (Q4995117) (← links)
- A General Framework of Rotational Sparse Approximation in Uncertainty Quantification (Q5052909) (← links)
- Sequential Active Learning of Low-Dimensional Model Representations for Reliability Analysis (Q5864079) (← links)
- Sparse Bayesian learning for complex‐valued rational approximations (Q6071370) (← links)
- A novel sparse polynomial chaos expansion technique with high adaptiveness for surrogate modelling (Q6072781) (← links)
- Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification (Q6639348) (← links)
- Data-driven projection pursuit adaptation of polynomial chaos expansions for dependent high-dimensional parameters (Q6663322) (← links)
