Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Constrained Dynamics with Gauss Principle adhering Gaussian
  Processes   The identification of the constrained dynamics of mechanical systems is often
challenging. Learning methods promise to ease an analytical analysis, but
require considerable amounts of data for training. We propose to combine
insights from analytical mechanics with Gaussian process regression to improve
the model's data efficiency and constraint integrity. The result is a Gaussian
process model that incorporates a priori constraint knowledge such that its
predictions adhere to Gauss' principle of least constraint. In return,
predictions of the system's acceleration naturally respect potentially
non-ideal (non-)holonomic equality constraints. As corollary results, our model
enables to infer the acceleration of the unconstrained system from data of the
constrained system and enables knowledge transfer between differing constraint
configurations.

12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga