no code implementations • 2 Nov 2023 • Vojtech Kejzlar, Léo Neufcourt, Witold Nazarewicz
To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models.
1 code implementation • 28 Mar 2020 • Vojtech Kejzlar, Tapabrata Maiti
With the advancements of computer architectures, the use of computational models proliferates to solve complex problems in many scientific applications such as nuclear physics and climate research.
no code implementations • 11 Feb 2020 • Vojtech Kejzlar, Léo Neufcourt, Witold Nazarewicz, Paul-Gerhard Reinhard
We study the information content of nuclear masses from the perspective of global models of nuclear binding energies.