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We present a universal, data-driven decomposition of chaos as an intermittently forced linear system.
Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources.
We propose an alternative data-driven method to infer networked nonlinear dynamical systems by using sparsity-promoting optimization to select a subset of nonlinear interactions representing dynam
SParSE++ is an event-oriented stochastic parameter search algorithm that features various leaping methods and improved interpolation over the existing algorithm SParSE.
We review standard model reduction techniques such as Proper Orthogonal Decomposition (POD) with Galerkin projection and Balanced POD (BPOD).