Related papers: BBNet: accurate neural network emulator for primor…
$f(R)$ gravity is one of the serious alternatives of general relativity having a large range of astronomical consequences. In this work, we study Big Bang Nucleosynthesis (BBN) in $f(R)$ gravity theory. We consider modification to gravity…
This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs), which is a probabilistic model capable of estimating the…
A new observation of D in a primordial gas cloud, made using the high resolution spectrograph at the Keck telescope, indicates an abundance $ D/H =(1.9-2.5) \times 10^{-4}$ \cite{SCHR}. Since deuterium is destroyed by stars, and the…
We examine the possibility that Big Bang Nucleosynthesis (BBN) may produce non-trivial amounts of $^6$Li. If a primordial component of this isotope could be observed, it would provide a new fundamental test of Big-Bang cosmology, as well as…
In extended quintessence models, a scalar field which couples to the curvature scalar R provides most of the energy density of the universe. We point out that such models can also lead naturally to a decrease in the primordial abundance of…
Bayesian method is capable of capturing real world uncertainties/incompleteness and properly addressing the over-fitting issue faced by deep neural networks. In recent years, Bayesian Neural Networks (BNNs) have drawn tremendous attentions…
We review the Cosmology and Physics underlying Primordial Nucleosynthesis and survey current observational data in order to compare the predictions of Big Bang Nucleosynthesis with the inferred primordial abundances. From this comparison we…
Abundances of light nuclei formed during primordial nucleosynthesis are predicted by Standard Big Bang Model. The latest data from WMAP, with precision's higher than ever before, provides a motivation to determine theoretical higher order…
Edge AI applications increasingly require models that can learn and adapt on-device with minimal energy budget. Traditional deep learning models, while powerful, are often overparameterized, energy-hungry, and dependent on cloud…
Using the cosmological constants derived from WMAP, the standard big bang nucleosynthesis (SBBN) predicts the light elements primordial abundances for 4He, 3He, D, 6Li and 7Li. These predictions are in satisfactory agreement with the…
The early, hot, dense, expanding Universe was a primordial reactor in which the light nuclides D, 3He, 4He and 7Li were synthesized in astrophysically interesting abundances. The challenge to the standard hot big bang model (Big Bang…
Accurate simulation of atomic systems has the potential to revolutionize the design of molecules and materials. Unfortunately, exact solutions of the Schr\"odinger equation scale as O(N!) and remain inaccessible for systems with more than a…
Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data distributions. In this paper, we demonstrate properly…
The boundary element method (BEM) provides an efficient numerical framework for solving multiple scattering problems in unbounded homogeneous domains, since it reduces the discretization to the domain boundaries, thereby condensing the…
Standard Big Bang Nucleosynthesis (BBN) predicts the abundances of the light elements in the early universe. Even if the overall agreement with the experimental data is good, still some discrepancies exist on the relic abundances of…
Predicting the structure of quantum many-body systems from the first principles of quantum mechanics is a common challenge in physics, chemistry, and material science. Deep machine learning has proven to be a powerful tool for solving…
In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as…
The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches to enabling deep neural networks (DNNs) in this domain,…
Accurate modeling of the inflationary gravitational waves (GWs) requires time-consuming, iterative numerical integrations of differential equations to take into account their backreaction on the expansion history. To improve computational…
We evaluate abundance anomalies generated in patches of the universe where the baryon-to-photon ratio was locally enhanced by possibly many orders of magnitude in the range $\eta = 10^{-10} - 10^{-1}$. Our study is motivated by the possible…