mlsanityData to 7 Oct 2026

Identifying the Dynamics of a System by Leveraging Data from Similar Systems

arXiv 2204.05446 ↗First posted 11 Apr 2022eess.SY cs.SY stat.MLcited by 15

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Abstract (arXiv)

We study the problem of identifying the dynamics of a linear system when one has access to samples generated by a similar (but not identical) system, in addition to data from the true system. We use a weighted least squares approach and provide finite sample performance guarantees on the quality of the identified dynamics. Our results show that one can effectively use the auxiliary data generated by the similar system to reduce the estimation error due to the process noise, at the cost of adding a portion of error that is due to intrinsic differences in the models of the true and auxiliary systems. We also provide numerical experiments to validate our theoretical results. Our analysis can be applied to a variety of important settings. For example, if the system dynamics change at some point in time (e.g., due to a fault), how should one leverage data from the prior system in order to learn the dynamics of the new system? As another example, if there is abundant data available from a simulated (but imperfect) model of the true system, how should one weight that data compared to the real data from the system? Our analysis provides insights into the answers to these questions.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 11 Apr 2022.

Builds on

10 of 22 references.

References · 10

  1. 2018Learning Without Mixing: Towards A Sharp Analysis of Linear System Identificationcited by 158
  2. 2017On the Sample Complexity of the Linear Quadratic Regulatorcited by 151
  3. 2018Near optimal finite time identification of arbitrary linear dynamical systemscited by 89
  4. 2018Non-asymptotic Identification of LTI Systems from a Single Trajectorycited by 77
  5. 2017Finite Time Identification in Unstable Linear Systemscited by 66
  6. 2019Learning Linear Dynamical Systems with Semi-Parametric Least Squarescited by 58
  7. 2020Non-asymptotic Identification of Linear Dynamical Systems Using Multiple Trajectoriescited by 38
  8. 2018Predicting with Proxies: Transfer Learning in High Dimensioncited by 24
  9. 2019Nonparametric Finite Time LTI System Identificationcited by 11
  10. 2022Learning the Dynamics of Autonomous Linear Systems From Multiple Trajectoriescited by 8

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Cited by year

Citing papers per year: 2022: 4; 2023: 3; 2024: 3; 2025: 5.

15 papers.

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  1. 2022FedSysID: A Federated Approach to Sample-Efficient System Identificationcited by 15
  2. 2022Meta-Learning of Neural State-Space Models Using Data From Similar Systemscited by 11
  3. 2023Learning Personalized Models with Clustered System Identificationcited by 10
  4. 2023Learning Dynamical Systems by Leveraging Data from Similar Systemscited by 8
  5. 2022Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approachcited by 5
  6. 2025Meta-Learning for Physically-Constrained Neural System Identificationcited by 5
  7. 2024MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Modelscited by 4
  8. 2024Combining Federated Learning and Control: A Surveycited by 2
  9. 2025Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraftcited by 2
  10. 2022Learning Decentralized Linear Quadratic Regulators with √T Regretcited by 1
  11. 2023Online Change Points Detection for Linear Dynamical Systems with Finite Sample Guaranteescited by 1
  12. 2025Federated Nonlinear System Identificationcited by 1
3 more
  1. 2024Joint Learning of Linear Dynamical Systems under Smoothness Constraintscited by 0
  2. 2025A State Alignment-Centric Approach to Federated System Identification: The FedAlign Frameworkcited by 0
  3. 2025Adversarially Robust Multitask Adaptive Controlcited by 0