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- W3034156240 abstract "Given a sequence of empirical distribution data (e.g. a movie of a spatiotemporal processsuch as a fluid flow), this work develops an ensemble data assimilation method to estimate thetransition probability that represents a finite approximation of the Frobenius-Perron operator.This allows a dynamical systems knowledge to be incorporated into a prior ensemble, which providessensible estimates in instances of limited observation. We demonstrate improved estimates over a constrained optimization approach (based on a quadratic programming problem) which does not impose a prior on the solution except for Markov properties. The estimated transitionprobability then enables several probabilistic analysis of dynamical systems. We focus only on the identification of coherent patterns from the estimated Markov transition to demonstrate its application as a proof-of-concept. To the best of our knowledge, there have not been manyworks on data-driven methods to identify coherent patterns from this type of data. While here the results are presented only in the context of dynamical systems applications, this work we present here has the potential to make a contribution in wider application areas that require theestimation of transition probabilities from a time-ordered spatio-temporal distribution data." @default.
- W3034156240 created "2020-06-12" @default.
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- W3034156240 date "2020-10-01" @default.
- W3034156240 modified "2023-10-14" @default.
- W3034156240 title "Ensemble-based method for the inverse Frobenius–Perron operator problem: Data-driven global analysis from spatiotemporal “Movie” data" @default.
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- W3034156240 doi "https://doi.org/10.1016/j.physd.2020.132603" @default.
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