ICNDM jointly learns a controlled neural vector field and a Riemannian metric that enforce input-to-state contraction, cutting long-horizon rollout error on chaotic oscillators and a PMSM drive.
Design of interval observers for non- autonomous systems under input disturbances,
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Learning Stable Controlled Dynamical Systems via Input-Contraction Neural Differential Models
ICNDM jointly learns a controlled neural vector field and a Riemannian metric that enforce input-to-state contraction, cutting long-horizon rollout error on chaotic oscillators and a PMSM drive.