Maximum-likelihood reprojections for reliable Koopman predictors of parametric nonlinear dynamical systems

Koopman-based methods leverage a nonlinear lifting to enable linear regression techniques. Consequently, system analysis, learning, and prediction are considered through the lens of this lifting resulting in a nonlinear manifold that is invariant under the action of the Koopman operator. Typically,