4.6 Article

Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Physics, Applied

A neural network model relating extraction current characteristics with optical emission spectra for the purpose of a digital twin of miniaturized ion thrusters

Wen-Jie Zhang et al.

Summary: This study investigates a neural network model for real-time monitoring of the relationship between grid voltage and extraction current in a miniaturized ion thruster. Experimental results show that the model accurately predicts thrust with less than 6% difference from the actual value.

JOURNAL OF PHYSICS D-APPLIED PHYSICS (2022)

Article Physics, Fluids & Plasmas

Deep learning for NLTE spectral opacities

G. Kluth et al.

PHYSICS OF PLASMAS (2020)

Article Nuclear Science & Technology

Progress Toward Interpretable Machine Learning-Based Disruption Predictors Across Tokamaks

C. Rea et al.

FUSION SCIENCE AND TECHNOLOGY (2020)

Article Nanoscience & Nanotechnology

Deep learning surrogate model for kinetic Landau-fluid closure with collision

Libo Wang et al.

AIP ADVANCES (2020)

Article Nuclear Science & Technology

Advancing Fusion with Machine Learning Research Needs Workshop Report

David Humphreys et al.

JOURNAL OF FUSION ENERGY (2020)

Article Physics, Fluids & Plasmas

Effects of nitrogen seeding in a tokamak plasma

Shrish Raj et al.

PHYSICS OF PLASMAS (2020)

Article Physics, Fluids & Plasmas

Avalanche mechanism for runaway electron amplification in a tokamak plasma

Christopher J. McDevitt et al.

PLASMA PHYSICS AND CONTROLLED FUSION (2019)

Article Chemistry, Multidisciplinary

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

Kunal Ghosh et al.

ADVANCED SCIENCE (2019)

Article Multidisciplinary Sciences

Predicting disruptive instabilities in controlled fusion plasmas through deep learning

Julian Kates-Harbeck et al.

NATURE (2019)

Article Physics, Fluids & Plasmas

Progress in disruption prevention for ITER

E. J. Strait et al.

NUCLEAR FUSION (2019)

Article Physics, Fluids & Plasmas

Generalized collision operator for fast electrons interacting with partially ionized impurities

L. Hesslow et al.

JOURNAL OF PLASMA PHYSICS (2018)

Article Physics, Fluids & Plasmas

Phase-space dynamics of runaway electrons in magnetic fields

Zehua Guo et al.

PLASMA PHYSICS AND CONTROLLED FUSION (2017)

Article Physics, Multidisciplinary

Melt damage to the JET ITER-like Wall and divertor

G. F. Matthews et al.

PHYSICA SCRIPTA (2016)

Article Materials Science, Multidisciplinary

Disruptions in ITER and strategies for their control and mitigation

M. Lehnen et al.

JOURNAL OF NUCLEAR MATERIALS (2015)

Article Optics

The Los Alamos suite of relativistic atomic physics codes

C. J. Fontes et al.

JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS (2015)

Article Physics, Fluids & Plasmas

Impurity seeding for tokamak power exhaust: from present devices via ITER to DEMO

A. Kallenbach et al.

PLASMA PHYSICS AND CONTROLLED FUSION (2013)

Article Physics, Fluids & Plasmas

Progress in the ITER Physics Basis - Chapter 1: Overview and summary

M. Shimada et al.

NUCLEAR FUSION (2007)

Article Materials Science, Multidisciplinary

Disruption mitigation with high-pressure noble gas injection

DG Whyte et al.

JOURNAL OF NUCLEAR MATERIALS (2003)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Physics, Fluids & Plasmas

Calculation of the radiative cooling coefficient for krypton in a low density plasma

KB Fournier et al.

NUCLEAR FUSION (2000)