2022 Review of Data-Driven Plasma Science

Abstract

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.

112 pages (including 700+ references), 44 figures, submitted to IEEE Transactions on Plasma Science as a part of the IEEE Golden Anniversary Special Issue

References in corpus (32)

• Conditional Generative Adversarial Nets
• Sparsity and Incoherence in Compressive Sampling
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
• Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
• Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
• Transformation of arbitrary distributions to the normal distribution with application to EEG test-retest reliability
• Predicting Solar Flares Using a Long Short-Term Memory Network
• Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Product and Random Forest Algorithm
• Coherent control of plasma dynamics
• Quantifying Translation-Invariance in Convolutional Neural Networks
• An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
• Deep neural network Grad-Shafranov solver constrained with measured magnetic signals
• Demonstration of stable long-term operation of a kilohertz laser-plasma accelerator
• Deep learning for plasma tomography using the bolometer system at JET
• Constructing a new predictive scaling formula for ITER's divertor heat-load width informed by a simulation-anchored machine learning
• SOFT: A synthetic synchrotron diagnostic for runaway electrons
• The Blind Implosion-Maker - Automated Inertial Confinement Fusion experiment design
• Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks
• Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network
• Robust, open-source removal of systematics in Kepler data
• Turbulence model reduction by deep learning
• Identifying magnetic reconnection in 2D Hybrid Vlasov Maxwell simulations with Convolutional Neural Networks
• Detecting Reconnection Events in Kinetic Vlasov Hybrid Simulations Using Clustering Techniques
• Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning
• Characterizing magnetic reconnection regions using Gaussian mixture models on particle velocity distributions
• Analysis of beam position monitor requirements with Bayesian Gaussian regression
• Bayesian Approach for Linear Optics Correction
• SpaceML: Distributed Open-source Research with Citizen Scientists for the Advancement of Space Technology for NASA
• Towards Accommodating Real-time Jobs on HPC Platforms
• Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
• Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuning

Cited by in corpus (9)

• Condensed Matter Systems Exposed to Radiation: Multiscale Theory, Simulations, and Experiment
• Fast Dynamic 1D Simulation of Divertor Plasmas with Neural PDE Surrogates
• Physics-separating artificial neural networks for predicting initial stages of Al sputtering and thin film deposition in Ar plasma discharges
• A multifidelity Bayesian optimization method for inertial confinement fusion design
• A data management system for machine learning research of tokamak
• Physics-separating artificial neural networks for predicting sputtering and thin film deposition of AlN in Ar/N${}_2$ discharges on experimental timescales
• Semantic Information Management in Low-Temperature Plasma Science and Technology with VIVO
• Machine-learned trends in mirror configurations in the Large Plasma Device
• Parallelized Real-time Physics Codes for Plasma Control on DIII-D