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20172026
most citedA machine learning approach for efficient uncertainty quantification using multiscale methods

84 citations · 207 across the 18 of their papers we have counts for

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Showing 2019Show all

2 papers · 1 filter

cs.CE2019

Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models

Alexander Tarakanov, Ahmed H. Elsheikh

Surrogate-modelling techniques including Polynomial Chaos Expansion (PCE) is commonly used for statistical estimation (aka. Uncertainty Quantification) of quantities of interests o…

cs.LG2019★ 2 cited

Parametrization of stochastic inputs using generative adversarial networks with application in geology

Shing Chan, Ahmed H. Elsheikh

We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating t…