AI RESEARCH
Trustworthy Koopman Operator Learning: Invariance Diagnostics and Error Bounds
arXiv CS.LG
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ArXi:2603.15091v1 Announce Type: cross Koopman operator theory provides a global linear representation of nonlinear dynamics and underpins many data-driven methods. In practice, however, finite-dimensional feature spaces induced by a user-chosen dictionary are rarely invariant, so closure failures and projection errors lead to spurious eigenvalues, misleading Koopman modes, and overconfident forecasts.