Universality in Approximation

Universality in approximation refers to the idea that certain mathematical tools or models can, with enough flexibility, closely mimic a wide variety of functions or behaviors. Essentially, these tools are versatile enough to approximate almost any related function within a desired level of accuracy. For example, some neural networks can approximate any computational process if they have enough parameters. This concept highlights the power and generality of specific models, showing they aren’t limited to just particular problems but can adapt to many different situations by appropriately adjusting their structure or parameters.