mlsanityData to 7 Oct 2026

On the adaptation of in-context learners for system identification

arXiv 2312.04083 ↗First posted 7 Dec 2023cs.LG cs.SY eess.SYcited by 5

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Abstract (arXiv)

In-context system identification aims at constructing meta-models to describe classes of systems, differently from traditional approaches that model single systems. This paradigm facilitates the leveraging of knowledge acquired from observing the behaviour of different, yet related dynamics. This paper discusses the role of meta-model adaptation. Through numerical examples, we demonstrate how meta-model adaptation can enhance predictive performance in three realistic scenarios: tailoring the meta-model to describe a specific system rather than a class; extending the meta-model to capture the behaviour of systems beyond the initial training class; and recalibrating the model for new prediction tasks. Results highlight the effectiveness of meta-model adaptation to achieve a more robust and versatile meta-learning framework for system identification.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 7 Dec 2023.

Builds on

14 of 23 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2022General-Purpose In-Context Learning by Meta-Learning Transformerscited by 102
  4. 2021Adaptive-Control-Oriented Meta-Learning for Nonlinear Systemscited by 38
  5. 2023Deep networks for system identification: a Surveycited by 35
  6. 2020dynoNet: a neural network architecture for learning dynamical systemscited by 28
  7. 2020Continuous-time system identification with neural networks: Model structures and fitting criteriacited by 28
  8. 2019Performance-oriented model learning for data-driven MPC designcited by 22
  9. 2023Can Transformers Learn Optimal Filtering for Unknown Systems?cited by 15
  10. 2022On the adaptation of recurrent neural networks for system identificationcited by 13
  11. 2023From system models to class models: An in-context learning paradigmcited by 13
  12. 2022Deep Subspace Encoders for Nonlinear System Identificationcited by 12
2 more
  1. 2022Meta-Learning of Neural State-Space Models Using Data From Similar Systemscited by 11
  2. 2019Tustin neural networks: a class of recurrent nets for adaptive MPC of mechanical systemscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 2.

5 papers.

  1. 2025Meta-Learning for Physically-Constrained Neural System Identificationcited by 5
  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. 2026Learning Nonlinear Systems In-Context: From Synthetic Data to Real-World Motor Controlcited by 0
  5. 2026Online learning of neural state-space modelscited by 0