The study of nuclear structure and its evolution across the nuclear chart remains one of the most fundamental and challenging problems in modern physics. Despite significant progress in theoretical modeling, certain regions of the nuclear landscape, particularly near the drip lines and among superheavy elements (SHE), continue to present substantial theoretical and experimental uncertainties.
Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) offer a promising new paradigm for modeling complex physical systems by uncovering nonlinear correlations embedded within nuclear data. These data-driven approaches have the potential to complement, and in some cases surpass, computationally intensive many-body methods through improved inference, uncertainty quantification, and the discovery of novel empirical relationships.
In this talk, I will present recent work carried out in this direction at Cochin University of Science and Technology, Kerala, India.