Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Physics-Enforced Modeling for Insertion Loss of Transmission Lines by
  Deep Neural Networks   In this paper, we investigate data-driven parameterized modeling of insertion
loss for transmission lines with respect to design parameters. We first show
that direct application of neural networks can lead to non-physics models with
negative insertion loss. To mitigate this problem, we propose two deep learning
solutions. One solution is to add a regulation term, which represents the
passive condition, to the final loss function to enforce the negative quantity
of insertion loss. In the second method, a third-order polynomial expression is
defined first, which ensures positiveness, to approximate the insertion loss,
then DeepONet neural network structure, which was proposed recently for
function and system modeling, was employed to model the coefficients of
polynomials. The resulting neural network is applied to predict the
coefficients of the polynomial expression. The experimental results on an
open-sourced SI/PI database of a PCB design show that both methods can ensure
the positiveness for the insertion loss. Furthermore, both methods can achieve
similar prediction results, while the polynomial-based DeepONet method is
faster than DeepONet based method in training time.

34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol