相关论文: Performance Improvements for Nuclear Reaction Netw…
We present the state-of-the-art single-zone nuclear reaction network WinNet that is capable of calculating the nucleosynthetic yields of a large variety of astrophysical environments and conditions. This ranges from the calculation of the…
Nuclear reaction rate ($\lambda$) is a significant factor in the process of nucleosynthesis. A multi-layer directed-weighted nuclear reaction network in which the reaction rate as the weight, and neutron, proton, $^4$He and the remainder…
Extreme benchmarks of ten or more places for the point kinetics equations for time dependent nuclear reactor power transients are rare. Therefore, to establish an extreme benchmark, we will employ a Taylor series with continuous analytical…
Simulations in stellar astrophysics involve the coupling of hydrodynamics and nuclear reactions under a wide variety of conditions, from simmering convective flows to explosive nucleosynthesis. Numerical techniques such as operator…
In $\beta$-decay studies the determination of the decay probability to the ground state of the daughter nucleus often suffers from large systematic errors. The difficulty of the measurement is related to the absence of associated delayed…
In this article we present robust, efficient and accurate fully implicit time-stepping schemes and nonlinear solvers for systems of reaction-diffusion equations. The applications of reaction-diffusion systems is abundant in the literature,…
The nuclear reaction network is usually studied via precise calculation of differential equation sets, and much research interest has been focused on the characteristics of nuclides, such as half-life and size limit. In this paper, however,…
We study the efficiency of a neural-net filter and deconvolution method for estimating jet energies and spectra in high-background reactions such as nuclear collisions at the relativistic heavy-ion collider and the large hadron collider.…
The technical breakthroughs of multiple detectors developed by Daya Bay and RENO collaborations have gotten great attention. Yet the optimal determination of neutrino mixing parameters from reactor data depends on the statistical method and…
With the rise of deep learning technology in practical applications, Convolutional Neural Networks (CNNs) have been able to assist humans in solving many real-world problems. To enhance the performance of CNNs, numerous network…
Nuclear Reaction Analysis with ${}^{3}$He holds the promise to measure Deuterium depth profiles up to large depths. However, the extraction of the depth profile from the measured data is an ill-posed inversion problem. Here we demonstrate…
We consider partitioned time integration for heterogeneous coupled heat equations. First and second order multirate, as well as time-adaptive Dirichlet-Neumann Waveform relaxation (DNWR) methods are derived. In 1D and for implicit Euler…
A new method for the measurement of sample reactivity worth in a fast neutron reactor named the inverse kinetics method is proposed in the paper. The sample reactivity worth could be obtained by measuring the reactivity step change in the…
Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well…
DD and DT reaction rates may be compared to determine plasma temperatures in the 10--200 eV range. Distinguishing neutrons from these two reactions is difficult when yields are low or unpredictable. Time of flight methods fail if the source…
We contrasted the performance of deep neural networks - Convolutional Neural Network (CNN) and Graph Neural Network (GNN) - to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This…
A method for integrating the chemical equations associated with nuclear combustion at high temperature is presented and extensively checked. Following the idea of E. M\"uller, the feedback between nuclear rates and temperature was taken…
The multi-messenger observation of the next galactic core-collapse supernova will shed light on the different physical processes involved in these energetic explosions. Good timing and pointing capabilities of neutrino detectors would help…
We demonstrate the use of neural networks to accelerate the reaction steps in the MAESTROeX stellar hydrodynamics code. A traditional MAESTROeX simulation uses a stiff ODE integrator for the reactions; here we employ a ResNet architecture…
There is a high demand for nuclear data in multidisciplinary subject like nuclear astrophysics. The two areas of nuclear physics which are most clearly related to one another are stellar evolution and nucleosynthesis. The necessity for…