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UID:node-3036985@mathematics.huji.ac.il
DTSTAMP:20210616T130000Z
DTSTART:20210616T130000Z
DTEND:20210616T140000Z
SUMMARY:Analysis Seminar **NOTE THE SPECIAL TIME**: Amir Sagiv (Columbia) — Local and Optimal Transport Perspectives on Uncertainty Quantification
DESCRIPTION:**Title**: Local and Optimal Transport Perspectives on Uncertainty Quantification

**Abstract**: In many scientific areas,deterministic models(e.g., differential equations)use numericalparameters.Inreal-world settings,however,suchparameters might be uncertain or noisy;amore comprehensive modelshould thereforeprovide a statistical description of the quantity of interest. Underlying this computational problem is a fundamentalquestion-iftwo "similar" functions push-forward the same measure,wouldthe new resulting measuresbeclose, and if so, in what sense? We will first show how the probability density function (PDF) of the quantity of interest can be approximated, using spectral and local methods. We will then discuss the limitations of PDF approximation, and present an alternativeviewpoint: through optimal transport theory, a Wasserstein-distance formulation of our problem yields a much simpler and widely applicable theory.
LOCATION:The link will be sent to you after registration
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