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arXiv · 2008.11274

Structure exploiting methods for fast uncertainty quantification in multiphase flow through heterogeneous media

Helen Cleaves·Alen Alexanderian·Bilal Saad
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Abstract

We present a computational framework for dimension reduction and surrogate modeling to accelerate uncertainty quantification in computationally intensive models with high-dimensional inputs and function-valued outputs. Our driving application is multiphase flow in saturated-unsaturated porous media in the context of radioactive waste storage. For fast input dimension reduction, we utilize an approximate global sensitivity measure, for function-value outputs, motivated by ideas from the active subspace methods. The proposed approach does not require expensive gradient computations. We generate an efficient surrogate model by combining a truncated Karhunen-Lo\'{e}ve (KL) expansion of the output with polynomial chaos expansions, for the output KL modes, constructed in the reduced parameter space. We demonstrate the effectiveness of the proposed surrogate modeling approach with a comprehensive set of numerical experiments, where we consider a number of function-valued (temporally or spatially distributed) QoIs.

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Helen Cleaves, Alen Alexanderian, Bilal Saad. 2020-08-22. Structure exploiting methods for fast uncertainty quantification in multiphase flow through heterogeneous media. https://arxiv.org/abs/2008.11274

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