A hybrid principal-vector pruning framework refines Koopman subspace invariance with error bounds and rank-one update efficiency for lifted linear prediction.
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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Subspace Pruning via Principal Vectors for Accurate Koopman-Based Approximations
A hybrid principal-vector pruning framework refines Koopman subspace invariance with error bounds and rank-one update efficiency for lifted linear prediction.
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Global Linearization of Parameterized Nonlinear Systems with Stable Equilibrium Point Using the Koopman Operator
Koopman operators provide a global linearization of parameterized nonlinear systems with stable equilibria into finite-dimensional linear systems that depend continuously on the parameter.