Related papers: FENDL: A library for fusion research and applicati…
The $^{232}$Th neutron-induced fission cross section was evaluated from 500 keV to 200 MeV. The experimental $^{232}$Th fission cross sections and their ratios to the $^{235,238}$U fission cross sections in the EXFOR library were reviewed…
In this work, we are presenting a new database of astrophysical interest, based on calculations performed with the nuclear reaction code TALYS. Four quantities are systematically calculated for over 8000 nuclides: cross sections, reaction…
The freud Python package is a powerful library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface to fast, parallelized C++ routines that run efficiently on…
The development of a package for the management of physics data is described: its design, implementation and computational benchmarks. This package improves the data management tools originally developed for Geant4 physics models based on…
Multi-task learning (MTL) aims to enhance the performance and efficiency of machine learning models by simultaneously training them on multiple tasks. However, MTL research faces two challenges: 1) effectively modeling the relationships…
Decentralized federated learning (DFL) is a collaborative machine learning framework for training a model across participants without a central server or raw data exchange. DFL faces challenges due to statistical heterogeneity, as…
We present the Neutron Scattered Spectra Tool (NeSST), an open-source Python package for rapidly constructing primary and singly scattered neutron spectra from inertial confinement fusion (ICF) implosions. NeSST evaluates primary spectra…
Measurements of neutron-induced fission cross sections and light-ion production are planned in the energy range 1-40 MeV at the upcoming Neutrons For Science (NFS) facility. In order to prepare our detector setup for the neutron beam with…
We present a minimal nuclear energy density functional (NEDF) called "SeaLL1" that has the smallest number of possible phenomenological parameters to date. SeaLL1 is defined by 7 significant phenomenological parameters, each related to a…
Deep Neural Networks (DNNs) have emerged as the core enabler of many major applications on mobile devices. To achieve high accuracy, DNN models have become increasingly deep with hundreds or even thousands of operator layers, leading to…
Deep Learning (DL) has emerged as a powerful tool for vulnerability detection, often outperforming traditional solutions. However, developing effective DL models requires large amounts of real-world data, which can be difficult to obtain in…
The development of a package for the management of physics data is described: its design, implementation and computational benchmarks. This package improves the data management tools originally developed for Geant4 physics models based on…
The Accurate fission data for nuclear safety (AlFONS) project aims at high precision measurements of fission yields, using the renewed IGISOL mass separator facility in combination with a new high current light ion cyclotron at the…
Kernel fusion is a popular and effective approach for combining multiple features that characterize different aspects of data. Traditional approaches for Multiple Kernel Learning (MKL) attempt to learn the parameters for combining the…
The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, designing and operating these systems is exceptionally difficult, given the complexity of the…
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their…
Nuclear fusion is the process that powers the sun, and it is one of the best hopes to achieve a virtually unlimited energy source for the future of humanity. However, reproducing sustainable nuclear fusion reactions here on Earth is a…
A filtered density function (FDF) model based on deep neural network (DNN), termed DNN-FDF, is introduced for large eddy simulation (LES) of turbulent flows involving conserved scalar transport. The primary objectives of this study are to…
We developed a set of EXFOR utility codes (ForEXy) to process the information of the experimental nuclear reaction data stored in the EXFOR library. We designed a new JSON format (J4) for the EXFOR library, and developed a code converting…
The GEneral description of Fission observables (GEF) model was developed to produce fission related nuclear data which are of crucial importance for basic and applied nuclear physics. The investigation of the performance of the GEF code is…