In nonlinear dynamic systems, a change in one place can trigger an outsized change elsewhere. The climate, the workings of the human brain, and the behavior of the electric grid are all examples—and all change dramatically over time. Because of their inherent unpredictability, dynamic systems like these are notoriously difficult to model.
Click here for original story, The catch-22s of reservoir computing: Researchers find overlooked weakness in powerful machine learning tool
Source: Phys.org