Related papers: Bayesian optimization and nonlocal effects method …
We develop a novel deep learning method for uncertainty quantification in stochastic partial differential equations based on Bayesian neural network (BNN) and Hamiltonian Monte Carlo (HMC). A BNN efficiently learns the posterior…
Bayesian neural networks (BNN) can estimate the uncertainty in predictions, as opposed to non-Bayesian neural networks (NNs). However, BNNs have been far less widely used than non-Bayesian NNs in practice since they need iterative NN…
Nuclear structure plays a significant role on the rapid neutron capture process (r-process) since shapes evolve with the emergence of shells and sub-shells. There was some indication in neighboring nuclei that we might find examples of a…
Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task. This is done by finding an optimal point estimate for the weights in every node.…
We have proposed in a series of previous papers a method to determine the effective axial-vector current coupling and the strength of the isoscalar proton-neutron pairing interaction for calculating the nuclear matrix elements of the…
The particle-number-conserving (PNC) method in the framework of the deformed shell model (DSM) is employed to study the properties of the newly discovered short-lived neutron-deficient nucleus 224Np and its {\alpha}-decay. The calculated…
The probability of $\alpha$ particle emission for some recently observed superheavy nuclei (SHN) are investigated. The $\alpha$-decay half lives of SHN are calculated in a quantum tunneling model with density dependent M3Y (DDM3Y) effective…
The recently observed $\alpha$-decay chain ${}^{108}\text{Xe}\to{}^{104}\text{Te}\to{}^{100}\text{Sn}$ [K.~Auranen \emph{et al.}, Phys.\ Rev.\ Lett.\ {\bf121}, 182501 (2018)] could provide valuable information on the $\alpha$ clustering in…
The cluster decay process in $^{270-318}$118 superheavy nuclei has been studied extensively within the Coulomb and proximity potential model (CPPM), thereby investigating the probable cluster decays from the various isotopes of $Z = 118$.…
The modes of decay for the even-even isotopes of superheavy nuclei of Z = 118 and 120 with neutron number $160 \leq N \leq 204$ are investigated in the framework of the axially deformed relativistic mean field model. The asymmetry parameter…
We present updated and improved Big Bang Nucleosynthesis (BBN) constraints on heavy, long-lived beyond the Standard Model (BSM) relics $\phi$ decaying into pairs of Standard Model particles, covering a comprehensive set of two-body decay…
Compared to point estimates calculated by standard neural networks, Bayesian neural networks (BNN) provide probability distributions over the output predictions and model parameters, i.e., the weights. Training the weight distribution of a…
We present the beta decay half-lives calculation for selected even even nuclei that decay through electron emission. The kinematical portion of the half-life calculation was performed using a recently introduced technique for computation of…
A systematic study on $\alpha$-decay half-lives for the nuclei in the range $93\leq Z \leq 118$ is done by employing various versions of proximity potentials. To obtain the more reliable results, the deformation terms are included up to…
We present a microscopic model for the calculation of alpha-decay half lives employing potentials obtained from relativistic and non-relativistic self-consistent mean-field models. The nuclear and Coulomb potentials are used to obtain the…
The large-scale shell-model calculations have been performed for the neutron-rich nuclei in the south region of $^{208}$Pb in the nuclear chart. The $\beta$-decay properties, such as the $\log ft$, average shape factor values, half-lives,…
Bayesian neural Networks (BNNs) are a promising method of obtaining statistical uncertainties for neural network predictions but with a higher computational overhead which can limit their practical usage. This work explores the use of high…
Systematic alpha-nucleus folding potentials are used to analyze alpha-decay half-lives of superheavy nuclei. Preformation factors of about several per cent are found for all nuclei under study. The systematic behavior of the preformation…
This paper investigates two prominent probabilistic neural modeling paradigms: Bayesian Neural Networks (BNNs) and Mixture Density Networks (MDNs) for uncertainty-aware nonlinear regression. While BNNs incorporate epistemic uncertainty by…
Based on the recent data in NUBASE2012, an improved empirical formula for evaluating the $\alpha$-decay half-lives is presented, in which the hindrance effect resulted from the change of the ground state spins and parities of parent and…