Analyzing phase transitions in minimal reservoir computers

Kurzfassung

Minimal reservoir computers are powerful machine learning algorithms that can accurately predict nonlinear systems [1]. They differ from traditional feedforward neural networks by not relying on randomness but instead utilizing linear optimization, which enables them to operate on small training datasets and requires minimal computational resources. Additionally, they can make accurate predictions for over ten Lyapunov times with certain parameterizations. However, we discovered that for certain parametrizations, the prediction fails. With only a few parameters, the phase transition between various parameterizations can be analyzed to comprehend the reasons behind the success of a prediction. We do that by analyzing the reconstructed, underlying equations. [1] H. Ma, D. Prosperino, et al., Sci. Rep., 13, 12970 (2023)