物理过程的信号在函数空间中聚集于低变异性区域，支持从少量样本快速泛化。

Real-world phenomena do not generate arbitrary variability: their signals concentrate on compact, low-variability subsets of functional space, enabling rapid generalisation from few examples. We formalise this principle through a deterministic functional-topological framework in which the set of valid realisations produced by a physical process forms a compact subset of a Banach space, endowed with stable invariants, a finite empirical radius, and an induced continuous perceptual functional. This geometry provides structural constraints on variability, conditions for identifiability, and support for generalisation from sparse evidence. We develop this framework and examine its empirical relevance across five real-world domains: electromechanical railway point machines, electrochemical battery discharge, physiological ECG signals, atmospheric solar irradiance, and geophysical tidal cycles. Where available, we also compare real data with corresponding deterministic simulators. Across these domains, the empirical radius and internal Hausdorff stability of the perceptual manifold saturate after surprisingly few samples, indicating that admissible signal families occupy compact, low-variability regions of function space. These results suggest that compact perceptual manifolds provide a useful organising principle for both physical processes and learned representations, and support deterministic functional topology as a promising framework for understanding perception and representation.