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A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems
This paper concerns the study of optimal (supremum and infimum) uncertainty bounds for systems where the input (or prior) probability measure is only partially/imperfectly known (e. g., with only statistical moments and/or on a coarse topology) rather than fully specified.
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