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Large Deviation Theory for Stochastic Partial Differential Equations: Modeling and Computational Aspects

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If you have a question about this talk, please contact Carola-Bibiane Schoenlieb.

I will explain how Large DeviationTheory (LDT) can be used to estimate various expectations over probability distributions of the solutions of stochastic partial differential equations (SPDEs) that arises e.g. in material sciences, fluid dynamics, and atmosphere/ocean science. In particular, I will show how scaling arguments made within the realm of LDT sometime permits to obtain useful prior information about the system’s behavior. I will also illustrate via examples that LDT enable calculations that are mostly out of reach of brute force simulations.

This talk is part of the Applied and Computational Analysis series.

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