mlsanityData to 7 Oct 2026

Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing

arXiv 2203.13294 ↗First posted 24 Mar 2022cs.LG cs.NE nlin.CDcited by 20

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Abstract (arXiv)

Forecasting the behavior of high-dimensional dynamical systems using machine learning requires efficient methods to learn the underlying physical model. We demonstrate spatiotemporal chaos prediction using a machine learning architecture that, when combined with a next-generation reservoir computer, displays state-of-the-art performance with a computational time 103-104 times faster for training process and training data set ~ 102 times smaller than other machine learning algorithms. We also take advantage of the translational symmetry of the model to further reduce the computational cost and training data, each by a factor of ~10.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 12 of its 39 references that are in the map. First posted on arXiv 24 Mar 2022.

Builds on

12 of 39 references.

References · 12

  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 132
  3. 2018Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Modelcited by 57
  4. 2020Lattice gauge equivariant convolutional neural networkscited by 45
  5. 2021Machine-learning hidden symmetriescited by 31
  6. 2022A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamicscited by 30
  7. 2020Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systemscited by 23
  8. 2021Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Componentscited by 18
  9. 2020Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learningcited by 17
  10. 2021Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformerscited by 15
  11. 2017Machine Learning, Deepest Learning: Statistical Data Assimilation Problemscited by 13
  12. 2021Symmetry-Aware Reservoir Computingcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 5; 2025: 6; 2026: 2.

20 papers.

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  1. 2022Learning unseen coexisting attractorscited by 12
  2. 2024Controlling Chaos Using Edge Computing Hardwarecited by 11
  3. 2023Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learningcited by 9
  4. 2024Infinite-dimensional next-generation reservoir computingcited by 9
  5. 2023Controlling Chaotic Maps using Next-Generation Reservoir Computingcited by 7
  6. 2023Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamicscited by 5
  7. 2024Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Datacited by 5
  8. 2024How more data can hurt: Instability and regularization in next-generation reservoir computingcited by 5
  9. 2023Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulencecited by 3
  10. 2023Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computingcited by 3
  11. 2023Artificial Intelligence for the Electron Ion Collider (AI4EIC)cited by 3
  12. 2025On the emergence of numerical instabilities in Next Generation Reservoir Computingcited by 3
8 more
  1. 2025Versatile Reservoir Computing for Heterogeneous Complex Networkscited by 1
  2. 2024Controlling Dynamical Systems into Unseen Target States Using Machine Learningcited by 0
  3. 2025Tailored minimal reservoir computing: on the bidirectional connection between nonlinearities in the reservoir and in datacited by 0
  4. 2025Using Echo-State Networks to Reproduce Rare Events in Chaotic Systemscited by 0
  5. 2025A tensor network approach for chaotic time series predictioncited by 0
  6. 2025Next-generation reservoir computing validated by classification taskcited by 0
  7. 2026Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training errorcited by 0
  8. 2026Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systemscited by 0