Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Physics-guided Deep Markov Models for Learning Nonlinear Dynamical
  Systems with Uncertainty   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 functions of the system are
parameterized via feed-forward neural networks (deep generative models).
However, due to the generalized and highly versatile formulation of neural
network functions, the learned latent space is often prone to lack physical
interpretation and structured representation. To address this, we bridge
physics-based state space models with Deep Markov Models, thus delivering a
hybrid modelling framework for unsupervised learning and identification for
nonlinear dynamical systems. The proposed framework takes advantage of the
expressive power of deep learning, while retaining the driving physics of the
dynamical system by imposing physics-driven restrictions on the side of the
latent space. We demonstrate the benefits of such a fusion in terms of
achieving improved performance on illustrative simulation examples and
experimental case studies of nonlinear systems. Our results indicate that the
physics-based models involved in the employed transition and emission functions
essentially enforce a more structured and physically interpretable latent
space, which is essential to generalization and prediction capabilities.

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The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom