Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Transport models are the main method to obtain physics information on the nuclear equation
of state and in-medium properties of particles from low to relativistic-energy heavy-ion …

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 …

This study employs the isospin-dependent Boltzmann-Uehling-Uhlenbeck model to simulate
intermediate-energy heavy-ion collisions between prolate nuclei Mg 24. The emphasis is on …

A machine-learning algorithm, Light Gradient Boosting Machine, was applied for the first
time to investigate the fundamental experimental observables in even-even nuclei over the …

Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on
the comparison of measured observables and transport model simulations. Only the …

A convolutional neural network-based classifier is elaborated to retrace the initial orientation
of deformed nucleus-nucleus collisions by integrating multiple typical experimental …

The research paradigm in physics has evolved through three distinct phases: empirical
observation and induction, theoretical modeling and deduction and computational numerical …

In this work, the Light Gradient Boosting Machine (LightGBM), which is a modern decision
tree based machine-learning algorithm, is used to study the fusion cross section (CS) of …