Related papers: The QSE-reduced \alpha Network
As the ultimate stage of stellar nucleosynthesis, and the source of the iron peak nuclei, silicon burning is important to our understanding of the evolution of massive stars and supernovae. Our reexamination of silicon burning, using…
The computational requirements posed by multi-dimensional simulations of type Ia supernovae make it difficult to incorporate complex nuclear networks to follow the release of nuclear energy along with the propagation of the flame. Instead,…
One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of…
We present a straightforward integration method to compute the abundance and temperature evolution in explosive scenarios. In this approach the thermal equation is implicitely coupled with chemical equations in order to avoid instabilities…
We fill a void in merging empirical and phenomenological characterisation of the dynamical phase transitions in complex systems by identifying three of them on real-life financial markets. We extract and interpret the empirical, numerical,…
Having examined the application of quasi-equilibrium to hydrostatic silicon burning in Paper I of this series, Hix & Thielemann (1996), we now turn our attention to explosive silicon burning. Previous authors have shown that for material…
Novel method of calculating Nuclear Statistical Equilibrium is presented. Basic equations are carefully solved using arbitrary precision arithmetic. Special interpolation procedure is then used to retrieve all abundances using tabulated…
Almost all of the elements heavier than hydrogen that are present in our solar system were produced by nuclear burning processes either in the early universe or at some point in the life cycle of stars. In all of these environments, there…
We present new analytic solutions for one-zone (fully mixed) chemical evolution models and explore their implications. In contrast to existing analytic models, we incorporate a realistic delay time distribution for Type Ia supernovae (SNIa)…
Quantum phase estimation (QPE) is a promising quantum algorithm for obtaining molecular ground-state energies with chemical accuracy. However, its computational cost, dominated by the Hamiltonian 1-norm $\lambda$ and the cost of the block…
Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation. However, prevailing energy evaluations often oversimplify,…
Machine learning potentials (MLP) have revolutionized the field of atomistic simulations by describing the atomic interactions with the accuracy of electronic structure methods at a small fraction of the costs. Most current MLPs construct…
This review concentrates on the two principle methods used to evolve nuclear abundances within astrophysical simulations, evolution via rate equations and via equilibria. Because in general the rate equations in nucleosynthetic applications…
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…
This work targets the automated minimum-energy optimization of Quantized Neural Networks (QNNs) - networks using low precision weights and activations. These networks are trained from scratch at an arbitrary fixed point precision. At…
A central question in evolutionary biology is how to quantitatively understand the dynamics of genetically diverse populations. Modeling the genotype distribution is challenging, as it ultimately requires tracking all correlations (or…
A quantitative and predictive microscopic theoretical framework that can describe reactions induced by $\alpha$ particles ($^4$He nuclei) and heavier projectiles is currently lacking. Such a framework would contribute to reducing…
Identification of cancer driver genes is fundamental for the development of targeted therapeutic interventions. The integration of mutational profiles with protein-protein interaction (PPI) networks offers a promising avenue for their…
We investigate the nucleosynthesis process in high-entropy ($s/k_{\rm B}\gtrsim100$) and very fast-expanding ($\tau_{\rm exp}\sim10^{-3}\ {\rm s}$) materials. In such a material with the electron fraction near 0.5, an interesting…
This contribution reviews the parallel dynamics of Q-Ising neural networks for various architectures: extremely diluted asymmetric, layered feedforward, extremely diluted symmetric, and fully connected. Using a probabilistic signal-to-noise…