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On Fractional Moment Estimation from Polynomial Chaos Expansion
The proposed approach is utilized for an estimation of statistical moments and probability distributions in three numerical examples of increasing complexity.
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
We present a novel physics-constrained polynomial chaos expansion as a surrogate modeling method capable of performing both scientific machine learning (SciML) and uncertainty quantification (UQ) tasks.
Physics-Informed Polynomial Chaos Expansions
This paper presents a novel methodology for the construction of physics-informed polynomial chaos expansions (PCE) that combines the conventional experimental design with additional constraints from the physics of the model.
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion
The numerical results show the superiority of the DAL-PCE in comparison to (i) a single global polynomial chaos expansion and (ii) the recently proposed stochastic spectral embedding (SSE) method developed as an accurate surrogate model and which is based on a similar domain decomposition process.
