98 citations · 303 across the 13 of their papers we have counts for
8 papers · 1 filter
Statistical Global Model of beta- Half-lives and r-Process Nucleosynthesis
N. J. Costiris, E. Mavrommatis, K. A. Gernoth +1
Purpose: Our objective is to apply an improved statistical global model of beta^- decay half-life systematics [1] generated by machine-learning techniques to the prediction of beta…
Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines
N. J. Costiris, E. Mavrommatis, K. A. Gernoth +2
In this work, the beta-decay halflives problem is dealt as a nonlinear optimization problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing pas…
Decoding Beta-Decay Systematics: A Global Statistical Model for Beta^- Halflives
N. J. Costiris, E. Mavrommatis, K. A. Gernoth +1
Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theo…
A Global Model of -Decay Half-Lives Using Neural Networks
N. Costiris, E. Mavrommatis, K. A. Gernoth +1
Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that a…
One and two proton separation energies from nuclear mass systematics using neural networks
S. Athanassopoulos, E. Mavrommatis, K. A. Gernoth +1
We deal with the systematics of one and two proton separation energies as predicted by our latest global model for the masses of nuclides developed with the use of neural networks.…
Modeling Nuclear Properties with Support Vector Machines
Haochen Li, J. W. Clark, E. Mavrommatis +2
We have made initial studies of the potential of support vector machines (SVM) for providing statistical models of nuclear systematics with demonstrable predictive power. Using SVM…