Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Physically Interpretable Machine Learning for nuclear masses   We present a novel approach to modeling the ground state mass of atomic
nuclei based directly on a probabilistic neural network constrained by relevant
physics. Our Physically Interpretable Machine Learning (PIML) approach
incorporates knowledge of physics by using a physically motivated feature space
in addition to a soft physics constraint that is implemented as a penalty to
the loss function. We train our PIML model on a random set of $\sim$20\% of the
Atomic Mass Evaluation (AME) and predict the remaining $\sim$80\%. The success
of our methodology is exhibited by the unprecedented
$\sigma_\textrm{RMS}\sim186$ keV match to data for the training set and
$\sigma_\textrm{RMS}\sim316$ keV for the entire AME with $Z \geq 20$. We show
that our general methodology can be interpreted using feature importance.

27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
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
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga