TR2023-087

Meta-Learning of Neural State-Space Models Using Data From Similar Systems


    •  Chakrabarty, A., Wichern, G., Laughman, C.R., "Meta-Learning of Neural State-Space Models Using Data From Similar Systems", World Congress of the International Federation of Automatic Control (IFAC), July 2023.
      BibTeX TR2023-087 PDF
      • @inproceedings{Chakrabarty2023jul,
      • author = {Chakrabarty, Ankush and Wichern, Gordon and Laughman, Christopher R.},
      • title = {Meta-Learning of Neural State-Space Models Using Data From Similar Systems},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2023,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2023-087}
      • }
  • MERL Contacts:
  • Research Areas:

    Control, Machine Learning, Multi-Physical Modeling

Abstract:

Deep neural state-space models (SSMs) provide a powerful tool for modeling dynamical systems solely using operational data. Typically, neural SSMs are trained using data collected from the actual system under consideration, despite the likely existence of operational data from similar systems which have previously been deployed in the field. In this paper, we propose the use of model-agnostic meta-learning (MAML) for constructing deep encoder network-based SSMs, by leveraging a combination of archived data from similar systems (used to meta-train offline) and limited data from the actual system (used for rapid online adaptation). We demonstrate using a numerical example that meta-learning can result in more accurate neural SSM models than supervised- or transfer-learning, despite few adaptation steps and limited online data. Additionally, we show that by carefully partitioning and adapting the encoder layers while fixing the state-transition operator, we can achieve comparable performance to MAML while reducing online adaptation complexity.

 

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  • Related Publication

  •  Chakrabarty, A., Wichern, G., Laughman, C.R., "Meta-Learning of Neural State-Space Models Using Data From Similar Systems", arXiv, November 2022.
    BibTeX arXiv
    • @article{Chakrabarty2022nov3,
    • author = {Chakrabarty, Ankush and Wichern, Gordon and Laughman, Christopher R.},
    • title = {Meta-Learning of Neural State-Space Models Using Data From Similar Systems},
    • journal = {arXiv},
    • year = 2022,
    • month = nov,
    • url = {https://arxiv.org/abs/2211.07768}
    • }