The relative root-mean-square error of finite-dimensional Koopman Control Family predictors is strictly upper-bounded by the square root of the largest eigenvalue of the newly defined control forward-backward consistency matrix.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
Control Forward-Backward Consistency: Quantifying the Accuracy of Koopman Control Family Models
The relative root-mean-square error of finite-dimensional Koopman Control Family predictors is strictly upper-bounded by the square root of the largest eigenvalue of the newly defined control forward-backward consistency matrix.
-
Subspace Pruning via Principal Vectors for Accurate Koopman-Based Approximations
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.