IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-50905-w.html
   My bibliography  Save this article

Learning noise-induced transitions by multi-scaling reservoir computing

Author

Listed:
  • Zequn Lin

    (University of Electronic Science and Technology of China
    Beijing Normal University
    Westlake University
    Westlake University)

  • Zhaofan Lu

    (Beijing Normal University)

  • Zengru Di

    (Beijing Normal University)

  • Ying Tang

    (University of Electronic Science and Technology of China
    Beijing Normal University
    University of Electronic Science and Technology of China)

Abstract

Noise is usually regarded as adversarial to extracting effective dynamics from time series, such that conventional approaches usually aim at learning dynamics by mitigating the noisy effect. However, noise can have a functional role in driving transitions between stable states underlying many stochastic dynamics. We find that leveraging a machine learning model, reservoir computing, can learn noise-induced transitions. We propose a concise training protocol with a focus on a pivotal hyperparameter controlling the time scale. The approach is widely applicable, including a bistable system with white noise or colored noise, where it generates accurate statistics of transition time for white noise and specific transition time for colored noise. Instead, the conventional approaches such as SINDy and the recurrent neural network do not faithfully capture stochastic transitions even for the case of white noise. The present approach is also aware of asymmetry of the bistable potential, rotational dynamics caused by non-detailed balance, and transitions in multi-stable systems. For the experimental data of protein folding, it learns statistics of transition time between folded states, enabling us to characterize transition dynamics from a small dataset. The results portend the exploration of extending the prevailing approaches in learning dynamics from noisy time series.

Suggested Citation

  • Zequn Lin & Zhaofan Lu & Zengru Di & Ying Tang, 2024. "Learning noise-induced transitions by multi-scaling reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50905-w
    DOI: 10.1038/s41467-024-50905-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-50905-w
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-50905-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ying Tang & Jing Liu & Jiang Zhang & Pan Zhang, 2024. "Learning nonequilibrium statistical mechanics and dynamical phase transitions," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. Bethany Lusch & J. Nathan Kutz & Steven L. Brunton, 2018. "Deep learning for universal linear embeddings of nonlinear dynamics," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    3. Daniel J. Gauthier & Erik Bollt & Aaron Griffith & Wendson A. S. Barbosa, 2021. "Next generation reservoir computing," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Keck, Felix & Jütte, Silke & Lenzen, Manfred & Li, Mengyu, 2022. "Assessment of two optimisation methods for renewable energy capacity expansion planning," Applied Energy, Elsevier, vol. 306(PA).
    2. Yang, J. & Primo, E. & Aleja, D. & Criado, R. & Boccaletti, S. & Alfaro-Bittner, K., 2022. "Implementing and morphing Boolean gates with adaptive synchronization: The case of spiking neurons," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. Ding, Jiaqi & Zhao, Pu & Liu, Changjun & Wang, Xiaofang & Xie, Rong & Liu, Haitao, 2024. "From irregular to continuous: The deep Koopman model for time series forecasting of energy equipment," Applied Energy, Elsevier, vol. 364(C).
    4. Zhiyuan Li & Zhongshao Li & Wei Tang & Jiaping Yao & Zhipeng Dou & Junjie Gong & Yongfei Li & Beining Zhang & Yunxiao Dong & Jian Xia & Lin Sun & Peng Jiang & Xun Cao & Rui Yang & Xiangshui Miao & Ron, 2024. "Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    5. Chen, Zhong & Chen, Xiaofang & Liu, Jinping & Cen, Lihui & Gui, Weihua, 2024. "Learning model predictive control of nonlinear systems with time-varying parameters using Koopman operator," Applied Mathematics and Computation, Elsevier, vol. 470(C).
    6. Konstantin Avchaciov & Marina P. Antoch & Ekaterina L. Andrianova & Andrei E. Tarkhov & Leonid I. Menshikov & Olga Burmistrova & Andrei V. Gudkov & Peter O. Fedichev, 2022. "Unsupervised learning of aging principles from longitudinal data," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Graciela Schiliuk & Iader Giraldo, 2021. "Regional responses to the Covid-19 crisis: a comparative study from economic, policy, and institutional perspectives," Documentos de Discusión FLAR 19734, Fondo Latino Americano de Reservas - FLAR.
    8. Alen Senanian & Sridhar Prabhu & Vladimir Kremenetski & Saswata Roy & Yingkang Cao & Jeremy Kline & Tatsuhiro Onodera & Logan G. Wright & Xiaodi Wu & Valla Fatemi & Peter L. McMahon, 2024. "Microwave signal processing using an analog quantum reservoir computer," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    9. Gong, Xun & Wang, Xiaozhe & Cao, Bo, 2023. "On data-driven modeling and control in modern power grids stability: Survey and perspective," Applied Energy, Elsevier, vol. 350(C).
    10. Rijwan Khan, 2023. "Deep Learning System and It’s Automatic Testing: An Approach," Annals of Data Science, Springer, vol. 10(4), pages 1019-1033, August.
    11. Minati, Ludovico & Bartels, Jim & Li, Chao & Frasca, Mattia & Ito, Hiroyuki, 2022. "Synchronization phenomena in dual-transistor spiking oscillators realized experimentally towards physical reservoirs," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    12. Tao Wu & Xiangyun Gao & Feng An & Xiaotian Sun & Haizhong An & Zhen Su & Shraddha Gupta & Jianxi Gao & Jürgen Kurths, 2024. "Predicting multiple observations in complex systems through low-dimensional embeddings," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    13. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    14. Gianluca Fabiani & Nikolaos Evangelou & Tianqi Cui & Juan M. Bello-Rivas & Cristina P. Martin-Linares & Constantinos Siettos & Ioannis G. Kevrekidis, 2024. "Task-oriented machine learning surrogates for tipping points of agent-based models," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    15. Mandal, Ankit & Tiwari, Yash & Panigrahi, Prasanta K. & Pal, Mayukha, 2022. "Physics aware analytics for accurate state prediction of dynamical systems," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    16. Lina Jaurigue & Kathy Lüdge, 2022. "Connecting reservoir computing with statistical forecasting and deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-3, December.
    17. Mattia Cenedese & Joar Axås & Bastian Bäuerlein & Kerstin Avila & George Haller, 2022. "Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    18. Robert M. Kent & Wendson A. S. Barbosa & Daniel J. Gauthier, 2024. "Controlling chaos using edge computing hardware," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    19. Berry, Christopher & Douglas Hoffman, K., 2023. "Communicating intent: Effects of employer-controlled tipping strategy disclosures on tip amount and firm evaluations," Journal of Business Research, Elsevier, vol. 160(C).
    20. Ruomin Zhu & Sam Lilak & Alon Loeffler & Joseph Lizier & Adam Stieg & James Gimzewski & Zdenka Kuncic, 2023. "Online dynamical learning and sequence memory with neuromorphic nanowire networks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50905-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.