Due to the exponential growth of the Hilbert space dimension with system size, the
simulation of quantum many-body systems has remained a persistent challenge until today …

α-clustering structure is a significant topic in light nuclei. A Bayesian convolutional neural
network (BCNN) is applied to classify initial nonclustered and clustered configurations …

By relating the charge multiplicity distribution and the temperature of a de-exciting nucleus
through a deep neural network, we propose that the charge multiplicity distribution can be …

The so-called contemporary AI revolution has reached every corner of the social, human
and natural sciences--physics included. In the context of quantum many-body physics, its …

Predicting thermal conductance of complex networks poses a formidable challenge in the
field of materials science and engineering. This challenge arises due to the intricate …

The recently introduced Gaussian Process State (GPS) provides a highly flexible, compact,
and physically insightful representation of quantum many-body states based on ideas from …

Residual production cross sections in spallation reactions are key data for nuclear physics
and related applications. Spallation reactions are very complex due to the wide range of …

Capturing the correlation emerging between constituents of many-body systems accurately
is one of the key challenges for the appropriate description of various systems whose …

We investigate the potential of supervised machine learning to propagate a quantum system
in time. While Markovian dynamics can be learned easily, given a sufficient amount of data …

Capturing the correlation emerging between constituents of many-body systems is one of
the key challenges to describe various quantum systems accurately. This thesis discusses …