Data-Driven Approximation of the Perron-Frobenius Operator Using the Wasserstein Metric
نویسندگان
چکیده
This manuscript introduces a regression-type formulation for approximating the Perron-Frobenius Operator by relying on distributional snapshots of data. These may represent densities particles. The Wasserstein metric is leveraged to define suitable functional optimization in space distributions. allows seeking dynamics so as interpolate flow function space. A first-order necessary condition optimality derived and utilized construct gradient algorithm. framework exemplified with numerical simulations.
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ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2022
ISSN: ['2405-8963', '2405-8971']
DOI: https://doi.org/10.1016/j.ifacol.2022.11.076