Related papers: Deep Learning assisted microwave-plasma interactio…

This paper is the first in a series devoted to the numerical study of nonlinear interactions of electromagnetic waves with plasma. We start with non-magnetized pair plasmas, where the primary processes are induced (Compton) scattering and…

Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty,…

Thermal Energy Storage (TES) using Phase Change Materials (PCMs) represents a critical technology for sustainable energy management and grid stability. This study presents a novel Physics-Driven Deep Learning (PDDL) framework for modeling…

Despite their ubiquity, the rich physics present in a plasma sheath has inhibited the development of a generally applicable description of this critical region. The present study utilizes a physics-informed neural network (PINN) to evaluate…

Warm dense matter (WDM)---an extreme state with high temperatures and densities that occurs e.g. in astrophysical objects---constitutes one of the most active fields in plasma physics and materials science. These conditions can be realized…

In the electron-driven fast-ignition approach to inertial confinement fusion, petawatt laser pulses are required to generate MeV electrons that deposit several tens of kilojoules in the compressed core of an imploded DT shell. We review…

The ion-electron coupling properties for a ion impurity in an electron gas and for a two component plasma are carried out on the basis of a regularized electron-ion potential removing the short-range Coulomb divergence. This work is largely…

In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of…

Deep Learning (DL) inversion is a promising method for real time interpretation of logging while drilling (LWD) resistivity measurements for well navigation applications. In this context, measurement noise may significantly affect inversion…

Nonintrusive measurements of plasma properties are essential to evaluate, and numerically simulate, the in-flight performance of electric propulsion systems. As a logical first step in the development of new diagnostic techniques, this work…

Collective effects, such as waves and instabilities, are integral to our understanding of most plasma phenomena. We have been able to study these in ultracold neutral plasmas by shaping the initial density distribution through spatial…

This study uses plasma-profile data from the EUROfusion pedestal database, focusing on the electron-temperature and electron-density profiles in the edge region of H-mode ELMy JET ITER-Like-Wall (ILW) pulses. We make systematic predictions…

Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices. Many DL inverse techniques have succeeded on a number of AEM design tasks, but…

We perform experiments in the Large Plasma Device (LAPD) at the University of California, Los Angeles, studying how different end-electrode biasing schemes modify the radial potential profile in the machine. We impose biasing profiles of…

In this article, we present a detailed characterisation of a multicusp-assisted inductively coupled RF plasma source for plasma immersion ion implantation (PIII). Using laser-induced fluorescence (LIF) and RF-compensated Langmuir probe…

The ultrashort time-integrated diagnosis of ions plays a vital role in high energy density physics research. However, it is extremely challenging to measure in experiment. Here, we demonstrate a reliable approach for investigating the…

Spectrum sensing is an essential component of modern wireless networks as it offers a tool to characterize spectrum usage and better utilize it. Deep Learning (DL) has become one of the most used techniques to perform spectrum sensing as…

The plasma density grating induced by intersecting intense laser pulses can be utilized as an optical compressors, polarizers, waveplates and photonic crystals for the manipulation of ultra-high-power laser pulses. However, the formation…

The deep-learning-based least squares method has shown successful results in solving high-dimensional non-linear partial differential equations (PDEs). However, this method usually converges slowly. To speed up the convergence of this…

The scientific computation of large deformations in elastic-plastic solids is crucial in various manufacturing applications. Traditional numerical methods exhibit several inherent limitations, prompting Deep Learning (DL) as a promising…