This work shows a phase diagram based on complexity–capacity inequality with random geometric graphs, indicating connections among semantic and dynamical complexities.
Key Points
The observed phase diagram includes random geometric graphs that visualize complex semantic connectivity and transitions.
A complexity–capacity inequality defines the relationship among geometric observation capacity, law–time, and interface complexities.
Analytical methods reveal connections between semantic percolation phenomena and laws of entropy–transport geometry.
The model illustrates how changes in dimensional complexity shape understanding in AI and complex learning systems.