Related papers: FENDL: A library for fusion research and applicati…
For validation and development of codes and for modeling isotope production in high power accelerators and APT Materials studies, we have produced experimental, calculated, and evaluated activation libraries for interaction of nucleons with…
The nonuniform fast Fourier transform (NUFFT) generalizes the FFT to off-grid data. Its many applications include image reconstruction, data analysis, and the numerical solution of differential equations. We present FINUFFT, an efficient…
The European Lead-Cooled Training Reactor (ELECTRA) has been proposed as a training reactor for fast systems within the Swedish nuclear program. It is a low-power fast reactor cooled by pure liquid lead. In this work, we propagate the…
Spallation-induced fission reactions, in combination with state-of-the-art dynamical calculations, provide a robust framework for studying the nuclear dissipation mechanism in nuclear matter over a wide range of deformations. Experimental…
The present contribution does not aim at replacing the huge and often excellent literature on DFT for atomic nuclei, but tries to provide an updated introduction to this topic. The goal would be, ideally, to help a fresh M.Sc. or Ph.D.…
The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model training on multi-task learning datasets. However, a comprehensive…
A proton activation data library for 682 nuclides from 1-H to 210-Po in the energy range from 150 MeV up to 1 GeV was developed. To calculate proton activation data, the MCNPX 2.6.0 and CASCADE/INPE codes were chosen. Different intranuclear…
The recent systematics of proton-induced fission cross sections is extended to a wider range of target nuclei and incident energies. Reasonable agreement with available experimental data is demonstrated. The extended systematics is employed…
Most existing federated learning methods assume that clients have fully labeled data to train on, while in reality, it is hard for the clients to get task-specific labels due to users' privacy concerns, high labeling costs, or lack of…
Theoretical modeling of nucleus-nucleus collision often is based on the nucleus-nucleus potential. One of the advanced methods for constructing this potential is the semi-microscopical double-folding model with the M3Y-Paris NN-forces.…
DiffEqFlux.jl is a library for fusing neural networks and differential equations. In this work we describe differential equations from the viewpoint of data science and discuss the complementary nature between machine learning models and…
Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when…
The materials engineering data base relevant to fusion irradiation is poorly populated and it has long been recognized that a fusion spectrum neutron source will be required, the facility IFMIF being the present proposal. Re- evaluation of…
Federated learning (FL) enables distributed devices to collaboratively train machine learning models while maintaining data privacy. However, the heterogeneous hardware capabilities of devices often result in significant training delays, as…
Efficient deployment of Deep Neural Networks (DNNs), such as Large Language Models (LLMs), on tensor accelerators is essential for maximizing computational efficiency in modern AI systems. However, achieving this is challenging due to the…
The $^{242}$Pu neutron-induced fission cross section was evaluated from 100 keV to 200 MeV. The experimental $^{242}$Pu and $^{235}$U fission cross sections and their ratios in the EXFOR library were reviewed and analysed by the…
The need to populate the fusion materials engineering data base has long been recognized, the IFMIF facility being the present proposed neutron source for this purpose. Re-evaluation of the regulatory approach for the EU proposed DEMO…
We propose to develop a high-energy heavy-ion experimental database and make it accessible to the scientific community through an on-line interace. This database will be searchable and cross-indexed with relevant publications, including…
This research explores the application of Deep Reinforcement Learning (DRL) to optimize the design of a nuclear fusion reactor. DRL can efficiently address the challenging issues attributed to multiple physics and engineering constraints…
The use of deep learning is facilitating a wide range of data processing tasks in many areas. The analysis of fusion data is no exception, since there is a need to process large amounts of data collected from the diagnostic systems attached…