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

We demonstrate that Bayesian machine learning can be used to treat the vast amount of
experimental fission data which are noisy, incomplete, discrepant, and correlated. To supply …

Bayesian machine learning is a promising tool for the evaluation of nuclear fission data but
its potential capability has not been fully realized. We attempt to optimize the performances …

Abstract Machine learning methods have proven to be powerful in various research fields. In
this paper, we show that research on radiation effects could benefit from such methods and …

Nuclear data are fundamental inputs to radiation transport codes used for reactor design
and criticality safety. The design of experiments to reduce nuclear data uncertainty has been …

The preequilibrium reaction mechanism is considered in the context of the exciton model. A
modification to the one-particle–one-hole state density is studied which can be interpreted …

The neutron-induced fission cross sections were simultaneously evaluated for the JENDL-5
library for 233,235 U and 239,241 Pu from 10 keV to 200 MeV and for 238U and 240Pu from …

We perform a comprehensive analysis of complete fusion cross section data with the aim to
derive, in a completely data-driven way, a model suitable to predict the integrated cross …

Abstract Machine Learning and Artificial Intelligence techniques are increasingly applied in
the nuclear field. In this way, Spanish companies and research institutions have been …

Compensating errors between nuclear data observables in a library can adversely impact
application simulations. The primary goal of the EUCLID project (Experiments Underpinned …