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On Improved Statistical Accuracy of Low-Order Polynomial Chaos Approximations

arXiv:2606.07912v1 Announce Type: new Abstract: Polynomial chaos expansions provide surrogate models for stochastic systems, with coefficients typically derived using Galerkin projection, stochastic collocation, or least squares approximation. These traditional approaches often fail to accurately capture statistical moments without resorting to high-order approximations. We propose a constrained optimization framework that modifies standard techniques to determine polynomial chaos...

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