compressive-sensing

Enhancing l1-Minimization Estimates of Polynomial Chaos Expansions using Basis Selection

In this paper we present a basis selection method that can be used with l1-minimization to adaptively determine the large coefficients of polynomial chaos expansions (PCE). The adaptive construction produces anisotropic basis sets that have more …

High-Dimensional Sparse Surrogate Construction via Bayesian Compressive Sensing

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Bayesian Compressive Sensing Framework for High-Dimensional Surrogate Construction

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Bayesian Compressive Sensing Framework for High-Dimensional Surrogate Model Construction

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Bayesian Compressive Sensing Framework for Sparse Representations of High-Dimensional Models

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Dimensionality Reduction for Complex Models via Bayesian Compressive Sensing

Uncertainty quantification in complex physical models is often challenged by the computational expense of these models. One often needs to operate under the assumption of sparsely available model simulations. This issue is even more critical when …

Bayesian Compressive Sensing and Dimensionality Reduction for High-Dimensional Models

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Uncertainty Quantification in High-Dimensional, Nonlinear and Expensive Models

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High-dimensional Polynomial Chaos Basis Selection with Bayesian Compressive Sensing

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Sparse Surrogate Model Construction via Compressive Sensing for High-Dimensional Complex Models

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