• Corpus ID: 239015913

Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty

@article{Liu2021PhysicsguidedDM,
  title={Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty},
  author={Wei Liu and Zhi-Lu Lai and Kiran Bacsa and Eleni N. Chatzi},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.08607}
}
  • Wei Liu, Zhi-Lu Lai, +1 author E. Chatzi
  • Published 16 October 2021
  • Computer Science, Physics, Mathematics
  • ArXiv
In this paper, we propose a probabilistic physics-guided framework, termed Physics-guided Deep Markov Model (PgDMM). The framework is especially targeted to the inference of the characteristics and latent structure of nonlinear dynamical systems from measurement data, where it is typically intractable to perform exact inference of latent variables. A recently surfaced option pertains to leveraging variational inference to perform approximate inference. In such a scheme, transition and emission… 

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