Related papers: Physics-Informed Machine Learning Approach to Mode…
The photon flux resulting from high-energy electron beam interactions with high field systems, such as in the upcoming FACET-II experiments at SLAC National Accelerator Laboratory, may give deep insight into the electron beam's underlying…
High-entropy alloys (HEAs) with multiple constituent elements have been extensively studied in the past 20 years due to their promising engineering application. Previous experimental and computational studies of HEAs focused mainly on…
The THERMOS toolkit has been developed to calculate radiative properties of plasmas. This article contains a brief survey of some of its key features used by calculation of opacities and emissivities and by analysis of specific experiments.…
Atomic hydrogen masers occur in recombination plasmas in sufficiently dense HII regions. These hydrogen recombination line (HRL) masers have been observed in a handful of objects to date and the analysis of the atomic physics involved has…
Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study…
Emission and absorption lines from elements heavier than helium (metals) represent one of our strongest probes of galaxy formation physics across nearly all redshifts accessible to observations. The vast majority of simulations that model…
A new modeling way of describing the continuous absorption of electromagnetic (EM) radiation in dense partially ionized hydrogen plasma is tested in this work. It is shown that the obtained results give a possibility of calculating spectral…
Machine learning (ML) can be used to construct surrogate models for the fast prediction of a property of interest. ML can thus be applied to chemical projects, where the usual experimentation or calculation techniques can take hours or days…
Extreme Learning Machines (ELM) provide a fast alternative to traditional gradient-based learning in neural networks, offering rapid training and robust generalization capabilities. Its theoretical basis shows its universal approximation…
A two-field gyrofluid model including ion finite Larmor radius (FLR) corrections, magnetic fluctuations along the ambient field and electron inertia is used to study two-dimensional reconnection in a low $\beta_e$ collisionless plasma, in a…
Radio frequency (RF) waves can provide heating, current and flow drive, as well as instability control for steady state operations of fusion experiments. A particle simulation model has been developed in this work to provide a…
This study explores the integration of machine learning (ML) techniques with large eddy simulation (LES) for predicting species mass fraction and flame characteristics in partially premixed turbulent jet flames. The LES simulations,…
Radiation reaction (RR) is a fundamental yet incompletely validated process in laser-particle interactions, since it lacks quantitatively definitive experimental verifications, especially the transition from classical to quantum regime.…
There is growing interest in using machine learning (ML) methods for structural metamodeling due to the substantial computational cost of traditional simulations. Purely data-driven strategies often face limitations in model robustness,…
For atoms embedded in dense plasma, the plasma screening effects will greatly alter their structure and dynamics, and then determine the radiation transport properties of the plasma. In the present work, a new statistical model is proposed…
In this work two important models of treating collisional absorption in a laser driven plasma are compared, the dielectric and the ballistic model. We will see that there exists a remarkable connection between these basic approaches which…
Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach…
The 1s2-1s2l lines are the most intense He-like ions lines. They are used as spectroscopic diagnostics for solar active regions as well as for different laboratory plasmas. Nowadays, it exits very high spectral resolution instruments and,…
Machine learning models have exhibited exceptional results in various domains. The most prevalent approach for learning is the empirical risk minimizer (ERM), which adapts the model's weights to reduce the loss on a training set and…
The research of innovative methods aimed at reducing costs and shortening the time needed for simulation, going beyond conventional approaches based on Monte Carlo methods, has been sparked by the development of collision simulations at the…