CNMc generalizes traditional cluster-based network models to unseen control parameters by regressing transition probabilities and times after aligning state spaces across conditions with a Procrustes transformation.
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WSINDYc-MPC identifies governing dynamics more robustly than benchmarks under high noise, enabling longer prediction horizons and lower tracking errors in fusion, drone, chaos, and aircraft control tasks.
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Control-oriented cluster-based reduced-order modelling
CNMc generalizes traditional cluster-based network models to unseen control parameters by regressing transition probabilities and times after aligning state spaces across conditions with a Procrustes transformation.
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WSINDy for Model Predictive Control with Applications to Fusion, Drones, and Chaos
WSINDYc-MPC identifies governing dynamics more robustly than benchmarks under high noise, enabling longer prediction horizons and lower tracking errors in fusion, drone, chaos, and aircraft control tasks.