Related papers: Bayesian neural network with autoencoder for model…

We combine nonlocal effects with Bayesian Neural Network (BNN) methods to enhance the prediction accuracy of $\alpha$ decay half-lives. The results indicate that accounting for nonlocal effects significantly impacts the half-life…

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…

Based on Extreme Gradient Boosting (XGBoost) framework optimized via Bayesian hyperparameter tuning, we investigated the {\alpha}-decay energy and half-life of superheavy nuclei. By incorporating key nuclear structural features-including…

Nuclear $\beta$ decay is a key process to understand the origin of heavy elements in the universe, while the accuracy is far from satisfactory for the predictions of $\beta$-decay half-lives by nuclear models up to date. In this letter, we…

A hybrid approach combining the Tabular Prior-data Fitted Network (TabPFN) with the Coulomb and Proximity Potential Model (CPPM) is developed to investigate $\alpha$-particle preformation factors $P_{\alpha}$ and their impact on…

A detailed study of $\alpha$-clusters decay is exhibited by incorporating crucial microscopic nuclear structure information into the estimations of half-life and preformation factor. For the first time, using the k-cross validation…

The preformation factor quantifies the probability of {\alpha} particles preforming on the surface of the parent nucleus in decay theory and is closely related to the study of {\alpha} clustering structure. In this work, a multilayer…

Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic…

The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. We explore a novel approach that combines…

Ab-initio calculations of nuclear masses, the binding energy and the $\alpha$ decay half-lives are intractable for heavy nucleus, because of the curse of dimensionality in many body quantum simulations as proton number($\mathrm{N}$) and…

In this work, the beta-decay halflives problem is dealt as a nonlinear optimization problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing past similar approaches, we have constructed sophisticated…

Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical…

$\alpha$ decay is a common and important process for natural radioactivity of heavy and superheavy nuclei. The $\alpha$ decay half-lives for even-even nuclei from Z=62 to Z=118 are systematically researched based on the two-potential…

In recent years, artificial neural network (ANN) has been successfully applied in nuclear physics and some other areas of physics. This study begins with the calculations of {\alpha}-decay half-lives for some neutron-deficient nuclei using…

For radioactive nuclear data, $\beta$ decay is one of the most important information and is applied to various fields. However, some of the $\beta$-decay data are not available due to experimental difficulties. From this respect,…

The $\alpha$- decay half-lives of the superheavy nuclei are systematically studied using different versions of proximity potential and a exact method to calculate Coulomb potential between spherical and deformed nuclei in the framework of…

We build and train the artificial neural network model (ANN) based on the experimental $\alpha$-decay energy ($Q_{\alpha}$) data. Besides decays between the ground states of parent and daughter nuclei, decays from the ground state of parent…

While offering a principled framework for uncertainty quantification in deep learning, the employment of Bayesian Neural Networks (BNNs) is still constrained by their increased computational requirements and the convergence difficulties…

Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We describe and study in these models a decomposition of predictive uncertainty into…

Uncertainties such as manufacturing tolerances cause performance variations in complex engineering systems, making robust design optimization (RDO) essential. However, simulation-based RDO faces high computational cost for statistical…