A Bayesian optimal experimental design framework with Gaussian approximation of expected information gain and surrogate Fisher information enables optimized uniaxial tests that significantly improve identifiability of history-dependent constitutive parameters over random designs.
Efficient D-Optimal Design of Experiments for Infinite- Dimensional Bayesian Linear Inverse Problems
2 Pith papers cite this work. Polarity classification is still indexing.
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The work formulates a sparsity-promoting inverse problem for source identification in tomographic sensing of chemical plumes using level-set representations of concentration thresholds.
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Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws
A Bayesian optimal experimental design framework with Gaussian approximation of expected information gain and surrogate Fisher information enables optimized uniaxial tests that significantly improve identifiability of history-dependent constitutive parameters over random designs.
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Sparsity-Driven Source Localization in Tomographic Sensing Applications
The work formulates a sparsity-promoting inverse problem for source identification in tomographic sensing of chemical plumes using level-set representations of concentration thresholds.