arxiveess.SY2026-07-07
Learning Stable Controlled Dynamical Systems via Input-Contraction Neural Differential Models
Learning continuous-time representations of dynamical systems from observation data has emerged as a cornerstone of data-driven control and scientific machine learning. However, existing neural differential equations either treat external control inputs heuristically without prov…