Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Linearly Constrained Neural Networks   We present a novel approach to modelling and learning vector fields from
physical systems using neural networks that explicitly satisfy known linear
operator constraints. To achieve this, the target function is modelled as a
linear transformation of an underlying potential field, which is in turn
modelled by a neural network. This transformation is chosen such that any
prediction of the target function is guaranteed to satisfy the constraints. The
approach is demonstrated on both simulated and real data examples.

31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph