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 (60)

• 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
• Machine learning accelerated computational fluid dynamics
• Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
• A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications
• 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
• The data-driven future of high energy density physics
• SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors
• Uncovering turbulent plasma dynamics via deep learning from partial observations
• 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
• Laser-accelerated, low divergence 15 MeV quasi-monoenergetic electron bunches at 1 kHz
• Multi-Objective Bayesian Optimization for Accelerator Tuning
• Machine learning for beam dynamics studies at the CERN Large Hadron Collider
• Deep learning for plasma tomography using the bolometer system at JET
• Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imaging
• 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
• Physics-informed Gaussian Process for Online Optimization of Particle Accelerators
• The Blind Implosion-Maker - Automated Inertial Confinement Fusion experiment design
• Neural network surrogate of QuaLiKiz using JET experimental data to populate training space
• Measuring the Electron Temperature and Identifying Plasma Detachment using Machine Learning and Spectroscopy
• Machine learning moment closure models for the radiative transfer equation I: directly learning a gradient based closure
• Deep learning based surrogate model for first-principles global simulations of fusion plasmas
• Using neural networks to accelerate the solution of the Boltzmann equation
• Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks
• Multiobjective optimization of the dynamic aperture for SLS 2.0 using surrogate models based on artificial neural networks
• Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data
• Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network
• Robust, open-source removal of systematics in Kepler data
• Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics
• DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction
• Turbulence model reduction by deep learning
• Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning
• Identifying magnetic reconnection in 2D Hybrid Vlasov Maxwell simulations with Convolutional Neural Networks
• Uncertainty Quantification for Virtual Diagnostic of Particle Accelerators
• Proof of concept of a fast surrogate model of the VMEC code via neural networks in Wendelstein 7-X scenarios
• Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems
• Detecting Reconnection Events in Kinetic Vlasov Hybrid Simulations Using Clustering Techniques
• Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning
• Detecting reconnection sites using the Lorentz Transformations for electromagnetic fields
• Beyond optimization -- supervised learning applications in relativistic laser-plasma experiments
• Characterizing magnetic reconnection regions using Gaussian mixture models on particle velocity distributions
• Analysis of beam position monitor requirements with Bayesian Gaussian regression
• Multi-Beam Energy Moments of Compound Measured Ion Velocity Distributions
• Bayesian Approach for Linear Optics Correction
• Billion-pixel X-ray camera (BiPC-X)
• SpaceML: Distributed Open-source Research with Citizen Scientists for the Advancement of Space Technology for NASA
• Near real-time streaming analysis of big fusion data
• 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