9 citations · 11 across the 3 of their papers we have counts for
3 papers
nucl-th2025★ 2 cited
Further exploration of binding energy residuals using machine learning and the development of a composite ensemble model
I. Bentley, J. Tedder, M. Gebran +1
This paper describes the development of the Four Model Tree Ensemble (FMTE). The FMTE is a composite of machine learning models trained on experimental binding energies from the At…
nucl-th2024★ 9 cited
High Precision Binding Energies from Physics Informed Machine Learning
Ian Bentley, James Tedder, Marwan Gebran +1
Twelve physics-informed machine learning models have been trained to model binding energy residuals. Our approach begins with determining the difference between measured experiment…
physics.data-an2005
A New Algorithm to Smooth Quantization Errors
Ayan Paul, P. K. Mukhopadhyay
We have devised a simple numerical technique to treat rugged data points that arise due to the insufficient gain setting error (or quantization error) of a digital instrument. This…