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Particle filters in highly nonlinear high-dimensional systems

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Inverse Problems

Bayes theorem formulates the data-assimilation problem as a multiplication problem and not an inverse problem. In this talk we exploit that using an extremely efficient particle filter on a highly nonlinear geophysical fluid flow problem of dimension 65,000. We show how collapse of the particles can be avoided, and discuss statistics showing that the particle filter is performing correctly.

This talk is part of the Isaac Newton Institute Seminar Series series.

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