mlsanityData to 7 Oct 2026

Machine-learning parameter tracking with partial state observation

arXiv 2311.09142 ↗First posted 15 Nov 2023cs.LG math.DS nlin.CDcited by 4

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Abstract (arXiv)

Complex and nonlinear dynamical systems often involve parameters that change with time, accurate tracking of which is essential to tasks such as state estimation, prediction, and control. Existing machine-learning methods require full state observation of the underlying system and tacitly assume adiabatic changes in the parameter. Formulating an inverse problem and exploiting reservoir computing, we develop a model-free and fully data-driven framework to accurately track time-varying parameters from partial state observation in real time. In particular, with training data from a subset of the dynamical variables of the system for a small number of known parameter values, the framework is able to accurately predict the parameter variations in time. Low- and high-dimensional, Markovian and non-Markovian nonlinear dynamical systems are used to demonstrate the power of the machine-learning based parameter-tracking framework. Pertinent issues affecting the tracking performance are addressed.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 13 of its 65 references that are in the map. First posted on arXiv 15 Nov 2023.

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13 of 65 references.

References · 13

  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. 2020Machine learning prediction of critical transition and system collapsecited by 54
  4. 2019Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computerscited by 43
  5. 2020Using machine learning to correct model error in data assimilation and forecast applicationscited by 40
  6. 2019Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radiuscited by 34
  7. 2021Anticipating synchronization with machine learningcited by 22
  8. 2021Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Componentscited by 18
  9. 2018Machine-learning inference of fluid variables from data using reservoir computingcited by 16
  10. 2019Dynamical learning of dynamicscited by 16
  11. 2022Optimizing Memory in Reservoir Computerscited by 13
  12. 2023Model-free tracking control of complex dynamical trajectories with machine learningcited by 13
1 more
  1. 2016Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networkscited by 10

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Citing papers per year: 2024: 4.

4 papers.

  1. 2024Data-driven model discovery with Kolmogorov-Arnold networkscited by 9
  2. 2024Learning to learn ecosystems from limited data -- a meta-learning approachcited by 2
  3. 2024Reconstructing dynamics from sparse observations with no training on target systemcited by 2
  4. 2024Prediction of Unobserved Bifurcation by Unsupervised Extraction of Slowly Time-Varying System Parameter Dynamics from Time Series Using Reservoir Computingcited by 0