Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

Helium line emission spectroscopy to measure plasma parameters using modeling and
machine learning in low-temperature plasmas - IOPscience Skip to content IOP Science home …

We use machine-learning (ML) models to predict ion density and electron temperature from
visible emission spectra, in a high-energy density pulsed-power-driven aluminum plasma …

We present a fast and accurate data-driven surrogate model for divertor plasma detachment
prediction leveraging the latent feature space concept in machine learning research. Our …

Optical emission spectroscopy (OES) of helium (He) line intensities has been used to
measure the electron density, ne, and temperature, T e, in various plasma devices. In this …

We present a Python-based numerical solver for the two-dimensional dynamic plasma
equilibrium problem. We model the time evolution of toroidally symmetric free-boundary …

We propose in this paper the use of artificial intelligence, especially deep learning
algorithms, for the isotopic ratio determination for hydrogen–deuterium mixtures. Our …

Abstract Machine learning methods were combined with differential absorption spectroscopy
measurements in the vacuum-ultraviolet region (5.167–9.920 eV) in order to develop …

Artificial Intelligence (AI) and data science techniques are increasingly introduced in physics
including plasma physics where Machine Learning (ML) is applied to emission spectroscopy …

The present work employs two advanced machine learning (ML) techniques: the Random
Forest (RF) model and Deep Neural Network (DNN) for the non-invasive spectroscopic …