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Data based identification and prediction of nonlinear and complex dynamical systems
2016
Physics reports
The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. In this paper, we review the recent advances in this forefront and rapidly evolving field, aiming to cover topics such as compressive sensing (a novel optimization paradigm for sparse-signal reconstruction), noised-induced dynamical mapping,
doi:10.1016/j.physrep.2016.06.004
fatcat:6lrzh3dzdfb6ljnyvawpif35ve