  Applying artificial intelligence to scientific problems (namely AI for
science) is currently under hot debate. However, the scientific problems differ
much from the conventional ones with images, texts, and etc., where new
challenges emerges with the unbalanced scientific data and complicated effects
from the physical setups. In this work, we demonstrate the validity of the deep
convolutional neural network (CNN) on reconstructing the lattice topology
(i.e., spin connectivities) in the presence of strong thermal fluctuations and
unbalanced data. Taking the kinetic Ising model with Glauber dynamics as an
example, the CNN maps the time-dependent local magnetic momenta (a single-node
feature) evolved from a specific initial configuration (dubbed as an evolution
instance) to the probabilities of the presences of the possible couplings. Our
scheme distinguishes from the previous ones that might require the knowledge on
the node dynamics, the responses from perturbations, or the evaluations of
statistic quantities such as correlations or transfer entropy from many
evolution instances. The fine tuning avoids the "barren plateau" caused by the
strong thermal fluctuations at high temperatures. Accurate reconstructions can
be made where the thermal fluctuations dominate over the correlations and
consequently the statistic methods in general fail. Meanwhile, we unveil the
generalization of CNN on dealing with the instances evolved from the unlearnt
initial spin configurations and those with the unlearnt lattices. We raise an
open question on the learning with unbalanced data in the nearly
"double-exponentially" large sample space.
