In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …

Abstract Machine learning (ML) is becoming a new paradigm for scientific research in
various research fields due to its exciting and powerful capability of modeling tools used for …

Although seemingly disparate, high-energy nuclear physics (HENP) and machine learning
(ML) have begun to merge in the last few years, yielding interesting results. It is worthy to …

The existence of quark matter inside the heaviest neutron stars has been the topic of
numerous recent studies, many of them suggesting that a phase transition to strongly …

Neutron star observables like masses, radii, and tidal deformability are direct probes to the
dense matter equation of state (EoS). A novel deep learning method that optimizes an EoS …

This study is devoted to the inference problem of extracting the nuclear matter properties
directly from a set of mass-radius observations. We employ Bayesian neural networks …

Neutron stars provide a unique opportunity to study strongly interacting matter under
extreme density conditions. The intricacies of matter inside neutron stars and their equation …

We discuss the machine-learning inference and uncertainty quantification for the equation of
state (EOS) of the neutron star matter directly using the NS probability distribution from the …

We exploit the great potential offered by Bayesian neural networks (BNNs) to directly
decipher the internal composition of neutron stars (NSs) based on their macroscopic …

The integration of deep learning techniques and physics-driven designs is reforming the
way we address inverse problems, in which accurate physical properties are extracted from …