Pages that link to "Item:Q1985532"
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The following pages link to Sparse polynomial chaos expansions via compressed sensing and D-optimal design (Q1985532):
Displaying 37 items.
- Sparse grids and applications -- Stuttgart 2014. Collected contributions of the 3rd workshop, SGA 2014, Stuttgart, Germany, September 1--5, 2014 (Q261496) (← links)
- A weighted \(\ell_1\)-minimization approach for sparse polynomial chaos expansions (Q349012) (← links)
- Compressive sampling of polynomial chaos expansions: convergence analysis and sampling strategies (Q349724) (← links)
- A priori testing of sparse adaptive polynomial chaos expansions using an ocean general circulation model database (Q1663464) (← links)
- A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (Q1987969) (← links)
- An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework (Q1988073) (← links)
- An efficient adaptive forward-backward selection method for sparse polynomial chaos expansion (Q1988232) (← links)
- M-PCM-OFFD: an effective output statistics estimation method for systems of high dimensional uncertainties subject to low-order parameter interactions (Q1997513) (← links)
- Optimal Bayesian experimental design for subsurface flow problems (Q2020243) (← links)
- Sequential sparse Bayesian learning with applications to system identification for damage assessment and recursive reconstruction of image sequences (Q2020862) (← links)
- Adaboost-based ensemble of polynomial chaos expansion with adaptive sampling (Q2060149) (← links)
- Novel algorithm for flexible multibody systems with hybrid uncertainties (Q2110864) (← links)
- Bi-fidelity reduced polynomial chaos expansion for uncertainty quantification (Q2115584) (← links)
- Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression (Q2174145) (← links)
- Adaptive weighted least-squares polynomial chaos expansion with basis adaptivity and sequential adaptive sampling (Q2175295) (← links)
- Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (Q2180429) (← links)
- Some greedy algorithms for sparse polynomial chaos expansions (Q2220572) (← links)
- Efficient uncertainty quantification of CFD problems by combination of proper orthogonal decomposition and compressed sensing (Q2243373) (← links)
- Variance-based adaptive sequential sampling for polynomial chaos expansion (Q2246308) (← links)
- A hybrid sequential sampling strategy for sparse polynomial chaos expansion based on compressive sampling and Bayesian experimental design (Q2246331) (← links)
- Divide and conquer: an incremental sparsity promoting compressive sampling approach for polynomial chaos expansions (Q2309799) (← links)
- A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions (Q2425261) (← links)
- Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis (Q2674097) (← links)
- On the influence of over-parameterization in manifold based surrogates and deep neural operators (Q2687573) (← links)
- Comparison of the performance and reliability between improved sampling strategies for polynomial chaos expansion (Q2688361) (← links)
- Sparse Recovery via <i>ℓ<sub>q</sub></i>-Minimization for Polynomial Chaos Expansions (Q3176044) (← links)
- Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions (Q3176252) (← links)
- Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark (Q4995117) (← links)
- Sparse Approximation of Data-Driven Polynomial Chaos Expansions: An Induced Sampling Approach (Q5142965) (← links)
- A novel sparse polynomial chaos expansion technique with high adaptiveness for surrogate modelling (Q6072781) (← links)
- A massively parallel implementation of multilevel Monte Carlo for finite element models (Q6094001) (← links)
- Sensitivity-enhanced generalized polynomial chaos for efficient uncertainty quantification (Q6095126) (← links)
- An active sparse polynomial chaos expansion approach based on sequential relevance vector machine (Q6120140) (← links)
- A new surrogate modeling method combining polynomial chaos expansion and Gaussian kernel in a sparse Bayesian learning framework (Q6549927) (← links)
- Active learning polynomial chaos expansion for reliability analysis by maximizing expected indicator function prediction error (Q6553474) (← links)
- Dimensional decomposition-aided metamodels for uncertainty quantification and optimization in engineering: a review (Q6566081) (← links)
- Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification (Q6639348) (← links)
