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Data-Driven Approximation of the Perron-Frobenius Operator Using the Wasserstein Metric (2011.00759v1)
Published 2 Nov 2020 in math.OC, cs.SY, and eess.SY
Abstract: This manuscript introduces a regression-type formulation for approximating the Perron-Frobenius Operator by relying on distributional snapshots of data. These snapshots may represent densities of particles. The Wasserstein metric is leveraged to define a suitable functional optimization in the space of distributions. The formulation allows seeking suitable dynamics so as to interpolate the distributional flow in function space. A first-order necessary condition for optimality is derived and utilized to construct a gradient flow approximating algorithm. The framework is exemplied with numerical simulations.