mlsanityData to 7 Oct 2026

dynoNet: a neural network architecture for learning dynamical systems

arXiv 2006.02250 ↗First posted 3 Jun 2020cs.LG cs.SY eess.SYcited by 28

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Abstract (arXiv)

This paper introduces a network architecture, called dynoNet, utilizing linear dynamical operators as elementary building blocks. Owing to the dynamical nature of these blocks, dynoNet networks are tailored for sequence modeling and system identification purposes. The back-propagation behavior of the linear dynamical operator with respect to both its parameters and its input sequence is defined. This enables end-to-end training of structured networks containing linear dynamical operators and other differentiable units, exploiting existing deep learning software. Examples show the effectiveness of the proposed approach on well-known system identification benchmarks. Examples show the effectiveness of the proposed approach against well-known system identification benchmarks.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2020.

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Citing papers per year: 2021: 2; 2022: 4; 2023: 6; 2024: 7; 2025: 5; 2026: 4.

28 papers.

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  1. 2023Deep networks for system identification: a Surveycited by 35
  2. 2021Constructing Neural Network-Based Models for Simulating Dynamical Systemscited by 29
  3. 2022On the adaptation of recurrent neural networks for system identificationcited by 13
  4. 2023From system models to class models: An in-context learning paradigmcited by 13
  5. 2022Meta-Learning of Neural State-Space Models Using Data From Similar Systemscited by 11
  6. 2024Differentiable All-pole Filters for Time-varying Audio Systemscited by 7
  7. 2024Baseline Results for Selected Nonlinear System Identification Benchmarkscited by 7
  8. 2022Learning neural state-space models: do we need a state estimator?cited by 6
  9. 2024Model order reduction of deep structured state-space models: A system-theoretic approachcited by 6
  10. 2022Continuous-time identification of dynamic state-space models by deep subspace encodingcited by 5
  11. 2023On the adaptation of in-context learners for system identificationcited by 5
  12. 2025Meta-Learning for Physically-Constrained Neural System Identificationcited by 5
16 more
  1. 2023Structured state-space models are deep Wiener modelscited by 4
  2. 2024Synthetic data generation for system identification: leveraging knowledge transfer from similar systemscited by 4
  3. 2024Enhanced Transformer architecture for in-context learning of dynamical systemscited by 4
  4. 2025dynoGP: Deep Gaussian Processes for dynamic system identificationcited by 3
  5. 2025Neural Entropy-stable conservative flux form neural networks for learning hyperbolic conservation lawscited by 3
  6. 2024Combining Federated Learning and Control: A Surveycited by 2
  7. 2025Efficient identification of linear, parameter-varying, and nonlinear systems with noise modelscited by 2
  8. 2021Deep learning with transfer functions: new applications in system identificationcited by 1
  9. 2025Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledgecited by 1
  10. 2026Tuning the burn-in phase in training recurrent neural networks improves their performancecited by 1
  11. 2026Controller Design for Structured State-space Models via Contraction Theorycited by 1
  12. 2023Orthogonal Transforms in Neural Networks Amount to Effective Regularizationcited by 0
  13. 2023Exploiting the capacity of deep networks only at training stage for nonlinear black-box system identificationcited by 0
  14. 2024Entropy stable conservative flux form neural networkscited by 0
  15. 2026Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamicscited by 0
  16. 2026Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identificationcited by 0