Software & Data Downloads — MetaLIC

Meta-Learning State Space Models for training and evaluating meta-learning for system identification and control via neural state-space models.

PyTorch implementation with Bouc-Wen nonlinear system identification benchmark using meta-learned neural state-space models for rapid adaptation.

    •  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), DOI: 10.1016/​j.ifacol.2023.10.1843, July 2023.
      BibTeX TR2023-087 PDF Software
      • @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,
      • doi = {10.1016/j.ifacol.2023.10.1843},
      • url = {https://www.merl.com/publications/TR2023-087}
      • }

    Access software at https://github.com/merlresearch/MetaLIC.