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@INPROCEEDINGS{Lagemann:276091,
      author       = {Lagemann, Kai and Lagemann, Christian and Mukherjee, Sach},
      title        = {{INVARIANCE}-{BASED} {LEARNING} {OF} {LATENT} {DYNAMICS}},
      reportid     = {DZNE-2025-00172},
      pages        = {200372},
      year         = {2024},
      abstract     = {We propose a new model class aimed at predicting dynamical
                      trajectories from highdimensional empirical data. This is
                      done by combining variational autoencodersand
                      (spatio-)temporal transformers within a framework designed
                      to enforce certainscientifically-motivated invariances. The
                      models allow inference of system behavior at any continuous
                      time and generalization well beyond the data
                      distributionsseen during training. Furthermore, the models
                      do not require an explicit neuralODE formulation, making
                      them efficient and highly scalable in practice. We
                      studybehavior through simple theoretical analyses and
                      extensive empirical experiments.The latter investigate the
                      ability to predict the trajectories of complicated
                      systemsbased on finite data and show that the proposed
                      approaches can outperform existingneural-dynamical models.
                      We study also more general inductive bias in the contextof
                      transfer to data obtained under entirely novel system
                      interventions. Overall, ourresults provide a new framework
                      for efficiently learning complicated dynamics in
                      adata-driven manner, with potential applications in a wide
                      range of fields includingphysics, biology, and engineering.},
      month         = {May},
      date          = {2024-05-07},
      organization  = {12th International Conference on
                       Learning Representations, Vienna
                       (Austria), 7 May 2024 - 11 May 2024},
      cin          = {AG Mukherjee},
      cid          = {I:(DE-2719)1013030},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://pub.dzne.de/record/276091},
}