Enhancing Sparsity of Hermite Polynomial Expansions by Iterative Rotations

Abstract

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 ($\mathcal{O}(100)$) problems.

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• Data-driven dimensional analysis: algorithms for unique and relevant dimensionless groups
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• Atomic radius and charge parameter uncertainty in biomolecular solvation energy calculations
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