Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty. (arXiv:2110.08607v3 [cs.LG] UPDATED)

In this paper, we propose a probabilistic physics-guided framework, termed
Physics-guided Deep Markov Model (PgDMM). The framework targets the inference
of the characteristics and latent structure of nonlinear dynamical systems from
measurement data, where exact inference of latent variables is typically
intractable. A recently surfaced option pertains to leveraging variational
inference to perform approximate inference. In such a scheme, transition and
emission functions of the system are parameterized via feed-forward neural
networks (deep generative models). However, due to the generalized …

arxiv learning markov physics systems uncertainty