Copy the dynamics using a learning machine
This provides a method for predicting and probing global dynamics in practical systems where equations are inaccessible, potentially applicable to complex systems like living brains.
The paper tackles the problem of simulating unknown dynamical systems without knowing their equations of motion, showing that a learning machine can copy and predict the dynamics of various black systems, including examples like the Lorenz system and a variable star.
Is it possible to generally construct a dynamical system to simulate a black system without recovering the equations of motion of the latter? Here we show that this goal can be approached by a learning machine. Trained by a set of input-output responses or a segment of time series of a black system, a learning machine can be served as a copy system to mimic the dynamics of various black systems. It can not only behave as the black system at the parameter set that the training data are made, but also recur the evolution history of the black system. As a result, the learning machine provides an effective way for prediction, and enables one to probe the global dynamics of a black system. These findings have significance for practical systems whose equations of motion cannot be approached accurately. Examples of copying the dynamics of an artificial neural network, the Lorenz system, and a variable star are given. Our idea paves a possible way towards copy a living brain.