Enhancing Sparsity of Hermite Polynomial Expansions by Iterative Rotations
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Abstract (arXiv)
Compressive sensing has become a powerful addition to uncertainty quantification in recent years. This paper identifies new bases for random variables through linear mappings such that the representation of the quantity of interest is more sparse with new basis functions associated with the new random variables. This sparsity increases both the efficiency and accuracy of the compressive sensing-based uncertainty quantification method. Specifically, we consider rotation-based linear mappings which are determined iteratively for Hermite polynomial expansions. We demonstrate the effectiveness of the new method with applications in solving stochastic partial differential equations and high-dimensional (O(100)) problems.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Jun 2015.
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Citing papers per year: 2022: 3.
3 papers.
- 2022MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problemscited by 13
- 2022On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samplescited by 8
- 2022RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantificationcited by 0
- 2022RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification11 Apr 2022
- 2022MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems7 Apr 2022
- 2022On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples25 Mar 2022