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Learning Noise-induced Transitions by Multi-scaling Reservoir Computing by Lin, Zequn; Lu, Zhaofan; Di, Zengru; Tang, Ying is a scholarly article available to read on EtoBox.

What is Learning Noise-induced Transitions by Multi-scaling Reservoir Computing about?

Noise is usually regarded as adversarial to extract the effective dynamics from time series, such that the conventional data-driven approaches usually aim at learning the dynamics by mitigating the noisy effect. However, noise can have a functional role of driving transitions between stable states underlying many natural and engineered stochastic dynamics. To capture such stochastic transitions from data, we find that leveraging a machine learning model, reservoir computing as a type of recurrent neural network, can learn noise-induced transitions. We develop a concise training protocol for tuning hyperparameters, with a focus on a pivotal hyperparameter controlling the time scale of the reservoir dynamics. The trained model generates accurate statistics of transition time and the number of transitions. The approach is applicable to a wide class of systems, including a bistable system under a double-well potential, with either white noise or colored noise. It is also aware of the asymmetry of the double-well potential, the rotational dynamics caused by non-detailed balance, and transitions in multi-stable systems. For the experimental data of protein folding, it learns the transiti

Author

Lin, Zequn; Lu, Zhaofan; Di, Zengru; Tang, Ying

Published

2023

Language

EN