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Projecting multiscale processes and an approach to clustering of directed networks

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Markov processes are widely used to model physical, chemical, or biological systems, and these processes often exhibit metastability. This leads to a multiscale behavior and the presence of rare events. We will discuss set-oriented and averaging-based approaches for time-reversible problems that can be connected to a multilevel Galerkin approximation of the transfer operator. Further, we will discuss possible extensions to the non-reversible case and an application to directed network clustering.

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

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