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Inference of Unknown Dynamical Components Using Next Generation Reservoir Computing: From Chaotic Systems to Climate Data

arXiv 2609.24754 ↗First posted 21 Sep 2026cs.LG nlin.CDcited by 0

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Abstract (arXiv)

We investigate next generation reservoir computing (NGRC) as a data-driven approach for inferring unseen components of dynamical systems. We compare NGRC with traditional reservoir computing (RC) using the Lorenz and R"ossler system, where two unknown components are inferred from one given component. For both systems, NGRC achieves accurate results while requiring fewer training data and less computational time than RC. We identified an inverse proportional behavior between the number of time-delayed steps needed for NGRC and the temporal resolution, indicating that the physical time span covered by the delay interval is an important factor in determining the required number of delayed steps. Finally, we apply NGRC to the observational climate data of ENSO (El Ni~no--Southern Oscillation) and infer one observable from the remaining variables. Despite the noise and complexity of the real-world data, the NGRC shows promising results. Our findings demonstrate the potential of NGRC for efficient inference of unseen components in both controlled dynamical systems and real-world data.

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References · 14

  1. 2021Next Generation Reservoir Computingcited by 160
  2. 2019Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamicscited by 134
  3. 2019Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computerscited by 43
  4. 2021Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observationscited by 21
  5. 2021Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computingcited by 21
  6. 2016Bayesian optimization of hyper-parameters in reservoir computingcited by 13
  7. 2019Efficient Optimization of Echo State Networks for Time Series Datasetscited by 10
  8. 2020Deep Time-Delay Reservoir Computing: Dynamics and Memory Capacitycited by 10
  9. 2020Accuracy of neural networks for the simulation of chaotic dynamics: precision of training data vs precision of the algorithmcited by 7
  10. 2019Performance boost of time-delay reservoir computing by non-resonant clock cyclecited by 6
  11. 2022Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordingscited by 5
  12. 2021Master memory function for delay-based reservoir computers with single-variable dynamicscited by 4
2 more
  1. 2023Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computingcited by 3
  2. 2025Long-term prediction of El Ni~no-Southern Oscillation using reservoir computing with data-driven realtime filtercited by 1

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