Related papers: Study on nuclear $\alpha$-decay energy by an artif…
We analyse and compare various empirical models of wall pressure spectra beneath turbulent boundary layers and propose an alternative machine learning approach using Artificial Neural Networks (ANN). The analysis and the training of the ANN…
Nuclear fission is still one of the most complex physical processes we can observe in nature due to the interplay of macroscopic and microscopic nuclear properties that decide the result. An example of this coupling is the presence of…
We investigate the shell structure of spherical nuclear bubbles in simple phenomenological shell model potentials. The shell correction energies for doubly magic bubbles may be as large as -40 MeV and probably imply a very long lifetime…
Ablation studies have been widely used in the field of neuroscience to tackle complex biological systems such as the extensively studied Drosophila central nervous system, the vertebrate brain and more interestingly and most delicately, the…
In the present paper we treat the second-forbidden non-unique (2nd-nu) ground-state-to-ground-state $\beta^-$ decay $^{99}\textrm{Tc}(9/2^+)\to\,^{99}\textrm{Ru}(5/2^+)$, with a 100$\%$ branching ratio, within the framework of the nuclear…
We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge radii. The model is trained using a multi-task approach with…
The aim of this paper is to address the question: Can an artificial neural network (ANN) model be used as a possible characterization of the power of the human mind? We will discuss what might be the relationship between such a model and…
Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices through asynchronous and sparse processing. However, being temporal models, SNNs depend…
We have investigated properties of $\alpha$-decay chains of recently produced superheavy elements Z=115 and Z=113 using the new Lagrangian model NL-SV1 with inclusion of the vector self-coupling of $\omega$ meson in the framework of the…
A high dimensional artificial neural network interatomic potential for Mo is developed. To train and validate the potential density functional theory calculations on structures and properties that correlate to fracture, such as elastic…
Measurements of the decay energy released as a function of time following the thermal neutron induced fission on $^{235}$U and $^{239,241}$Pu were performed in the 1970s at Oak Ridge National Laboratory with the purpose of quantifying…
A microscopic description of the interaction of atomic nuclei with external electroweak probes is required for elucidating aspects of short-range nuclear dynamics and for the correct interpretation of neutrino oscillation experiments.…
In this investigation, we compute the nuclear matrix elements (NMEs) relevant to the light neutrino-exchange mechanism governing neutrinoless double beta ($0\nu\beta\beta$) decay in $^{136}$Xe. Our method is based on the nonclosure approach…
New superheavy nuclei are often identified through their characteristic $\alpha$-decay energies, which requires accurate calculations of $Q_{\alpha}$ values. While many $Q_{\alpha}$ predictions are available, little is known about their…
A virtual chemical spectrophotometer for the simultaneous analysis of nickel (Ni) and cobalt (Co) was developed based on an artificial neural network (ANN). The developed ANN correlates the respective concentrations of Co and Ni given the…
In the study of $\alpha$ decay within the superheavy nuclear region ($Z \geq 90$ and $N \geq 140$), the $\alpha$-particle preformation probability $P_{\alpha}$ serves as a crucial physical quantity linking nuclear structure to decay…
In the present paper a newer application of Artificial Neural Network (ANN) has been developed i.e., predicting response-function results of electrical-mechanical system through ANN. This method is specially useful to complex systems for…
Artificial neural networks (ANNs) have been broadly utilized to analyze various data and solve different domain problems. However, neural networks (NNs) have been considered a black box operation for years because their underlying…
In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at different points of an ALD reactor. We introduce a dataset designed to train…
The prediction of near surface wind speed is becoming increasingly vital for the operation of electrical energy grids as the capacity of installed wind power grows. The majority of predictive wind speed modeling has focused on point-based…