Data-driven approximation methods are derived for the unitary Koopman-von Neumann operator, its eigenvalues and eigenfunctions, with explicit quantum-circuit representations for finite-dimensional projections.
Physica D: Non- linear Phenomena406, 132416 (2020) https://doi
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A tensor train method computes the Koopman generator via operator logarithm while preserving low-rank structure for scalable identification of high-dimensional nonlinear dynamics.
Koopman models identified via meta-heuristic EDMD from engine simulations enable an adaptive MPC with disturbance observer and a feedback linearization controller that achieve comparable steady-state performance with the adaptive version showing superior robustness under varying conditions.
citing papers explorer
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Numerical approximation of the Koopman-von Neumann equation: Operator learning and quantum computing
Data-driven approximation methods are derived for the unitary Koopman-von Neumann operator, its eigenvalues and eigenfunctions, with explicit quantum-circuit representations for finite-dimensional projections.
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Tensor-based computation of the Koopman generator via operator logarithm
A tensor train method computes the Koopman generator via operator logarithm while preserving low-rank structure for scalable identification of high-dimensional nonlinear dynamics.
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Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine
Koopman models identified via meta-heuristic EDMD from engine simulations enable an adaptive MPC with disturbance observer and a feedback linearization controller that achieve comparable steady-state performance with the adaptive version showing superior robustness under varying conditions.