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Learning noise-induced transitions by multi-scaling reservoir computing.

Zequn Lin1,2,3,4, Zhaofan Lu2, Zengru Di2

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.

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Summary
This summary is machine-generated.

This study shows that reservoir computing can learn noise-induced transitions in time series data, unlike conventional methods. This machine learning approach accurately captures stochastic transitions and dynamics from noisy datasets.

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Area of Science:

  • Complex Systems
  • Machine Learning
  • Time Series Analysis

Background:

  • Noise is typically viewed as detrimental to extracting dynamics from time series data.
  • Conventional methods often focus on noise reduction, potentially overlooking its functional role in driving system transitions.

Purpose of the Study:

  • To investigate the potential of machine learning, specifically reservoir computing, in learning noise-induced transitions.
  • To develop an effective training protocol for reservoir computing to capture stochastic dynamics.

Main Methods:

  • Utilized reservoir computing, a machine learning technique, to model time series dynamics.
  • Developed a concise training protocol focusing on a key hyperparameter for time scale control.
  • Applied the method to bistable systems with white and colored noise, and experimental protein folding data.

Main Results:

  • Reservoir computing successfully learned noise-induced transitions, outperforming conventional methods like SINDy and recurrent neural networks.
  • The approach accurately generated transition time statistics for white noise and specific transition times for colored noise.
  • Demonstrated applicability to asymmetric potentials, non-detailed balance dynamics, multi-stable systems, and protein folding dynamics from limited data.

Conclusions:

  • Machine learning, particularly reservoir computing, offers a powerful framework for learning dynamics from noisy time series, including noise-induced transitions.
  • The proposed method provides a robust tool for analyzing complex stochastic systems and characterizing transition dynamics.
  • This work opens avenues for extending existing approaches to dynamics learning in the presence of noise.