Related papers: Predicting Swarm Equatorial Plasma Bubbles via Mac…
We predict the imprint of linear bubbly perturbations on the polarization and temperature anisotropies of the cosmic microwave background (CMB). We analytically model a bubbly density perturbation at the beginning of the radiation dominated…
We present a new modeling tool for planetary nebulae, based on 3D photoionization calculations. Our goal is to show that all the information provided by observations, regarding kinematics and morphology, have to be consistently accounted…
The current suite of results from Cosmic Microwave Background anisotropy experiments is fulfilling the promise of providing extraordinary levels of discrimination between cosmological models. We calculate a binned anisotropy power spectrum,…
Simulating water from first principles remains a significant computational challenge due to the slow dynamics of the underlying system. Although machine-learned interatomic potentials (MLPs) can accelerate these simulations, they often fail…
Constructing the outer crust of the neutron stars requires the knowledge of the Binding Energy (BE) of the atomic nuclei. Although the BE of a lot of the nuclei is experimentally determined and can be obtained from the AME data table, for…
With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystems. This is, however, impeded by the complexity of…
The quality of astrochemical models is highly dependent on reliable binding energy (BE) values that consider the morphological and energetic variety of binding sites on the surface of ice-grain mantles. Here, we present the Binding Energy…
We propose a machine-learning-based (ML-based) method for efficiently predicting atomic diffusivity in crystals, in which the potential energy surface (PES) of a diffusion carrier is partially evaluated by first-principles calculations. To…
We apply the principles of quantum mechanics and quantum cosmology to predict probabilities for our local observations of a universe undergoing false vacuum eternal inflation. At a sufficiently fine-grained level, histories of the universe…
In this paper we develop a waveform model that accurately reproduces the dynamical evolution of intermediate-mass-ratio inspirals, as predicted by the effective-one-body (EOB) model introduced in [1], and which enables us to shed some light…
This paper illustrates the results obtained by using pre-trained semantic segmentation deep learning models for the detection of archaeological sites within the Mesopotamian floodplains environment. The models were fine-tuned using openly…
Approximate Bayesian computation (ABC) provides us with a way to infer parameters of models, for which the likelihood function is not available, from an observation. Using ABC, which depends on many simulations from the considered model, we…
The El Ni{~n}o-Southern Oscillation (ENSO) exerts profound influence on global climate variability, yet its prediction remains a grand challenge. Recent advances in deep learning have significantly improved forecasting skill, but the…
Explainable Artificial Intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP), have become essential tools for interpreting complex ensemble tree-based models, especially in high-stakes domains such as healthcare…
This article explores the estimation of unknown parameters and reliability characteristics under the assumption that the lifetimes of the testing units follow an Inverted Exponentiated Pareto (IEP) distribution. Here, both point and…
The solvation free energy (SFE) of molecules and ions is a fundamental property governing their solvation behavior and solubility. Molecular simulations offer a route to compute SFEs using alchemical free energy methods, such as…
Earth's magnetosphere is vital for today's technologically dependent society. To date, numerous design studies have been conducted and over a dozen science missions have own to study the magnetosphere. However, a majority of these solutions…
The East Lansing Model is a global, uncertainty-quantified optical potential for neutron and proton projectiles, with a novel form for the neutron-proton asymmetry component, with the goal to improve extrapolations away from stability. Our…
We compute the B-mode polarization power spectrum of the CMB from an epoch of inhomogeneous reionization, using a simple model in which HII regions are represented by ionized spherical bubbles with a log normal distribution of sizes whose…
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve…