Pages that link to "Item:Q2309799"
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The following pages link to Divide and conquer: an incremental sparsity promoting compressive sampling approach for polynomial chaos expansions (Q2309799):
Displaying 16 items.
- Enhancing sparsity of Hermite polynomial expansions by iterative rotations (Q729367) (← links)
- A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (Q1987969) (← links)
- Sequential sparse Bayesian learning with applications to system identification for damage assessment and recursive reconstruction of image sequences (Q2020862) (← links)
- A multi-fidelity polynomial chaos-greedy Kaczmarz approach for resource-efficient uncertainty quantification on limited budget (Q2072434) (← links)
- Some greedy algorithms for sparse polynomial chaos expansions (Q2220572) (← links)
- On the construction of uncertain time series surrogates using polynomial chaos and Gaussian processes (Q2292255) (← links)
- Basis adaptive sample efficient polynomial chaos (BASE-PC) (Q2425254) (← links)
- A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions (Q2425261) (← links)
- PI-VAE: physics-informed variational auto-encoder for stochastic differential equations (Q2679439) (← links)
- Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions (Q3176252) (← links)
- Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification (Q4611526) (← links)
- Using automatic differentiation for compressive sensing in uncertainty quantification (Q4685570) (← links)
- Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark (Q4995117) (← links)
- A General Framework of Rotational Sparse Approximation in Uncertainty Quantification (Q5052909) (← 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)
