A hybrid principal-vector pruning method is proposed to refine invariant subspaces for Koopman approximations, supported by error bounds on eigenfunction retention and a rank-one update scheme for efficient computation.
Data-driven feedback linearization using the Koopman generator
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Koopman operators provide a global linearization of parameterized nonlinear systems with stable equilibria into finite-dimensional linear systems that depend continuously on the parameter.
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