Integrated Data Analysis for Fusion: A Bayesian Tutorial for Fusion Diagnosticians

Dinklage, A.; Dreier, H.; Fischer, R.; Gori, S.; Preuss, R.; Toussaint, U. von

doi:10.1063/1.2905117

Cited by 7 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 12 shows the sampling results at 2060 ms of shot #31019, which present similar performance to the sampling results of DINKLAGE A [25]. Therefore, these results are reliable.…”

Section: Application To the Ts Diagnosticsupporting

confidence: 60%

Integrated data analysis on the electron temperature profile of HL-2A with the Bayesian probability inference method

Pan¹,

Wang²,

Wang³

et al. 2022

Plasma Sci. Technol.

View full text Add to dashboard Cite

The data analysis on tokamak plasmas is mainly based on various diagnostic systems, which are usually modularized and independent of each other. This leads to a large amount of data not fully and effectively exploited, so that it is not conducive to reveal the deep physical mechanism. In this paper, Bayesian probability inference (BPI) with machine learning methods has been applied to the electron cyclotron emission (ECE) and Thomson scattering (TS) diagnostic systems on HL-2A/2M, and the effects of integrated data analysis (IDA) on the electron temperature of HL-2A with Bayesian probability inference are demonstrated. A program is developed to infer the whole electron temperature profile with a confidence interval, and the program can be applied in the online analysis. The IDA results show that the full profile of the electron temperature can be obtained and the diagnostic information is more comprehensive and abundant with IDA. The inference models for electron temperature analysis are established and the developed programs will serve as an experimental data analysis tool of HL-2A/2M in the near future.

show abstract

“…Figure 12 shows the sampling results at 2060 ms of shot #31019, which present similar performance to the sampling results of DINKLAGE A [25]. Therefore, these results are reliable.…”

Section: Application To the Ts Diagnosticsupporting

confidence: 60%

Integrated data analysis on the electron temperature profile of HL-2A with the Bayesian probability inference method

Pan¹,

Wang²,

Wang³

et al. 2022

Plasma Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…electron temperature from line-integrated spectrometer measurements). Traditionally this statistical inference of physics parameters from diagnostic data has been performed under the umbrella of 'Integrated Data Analysis' [405], performing Bayesian analysis leveraging potentially multiple diagnostics. Recent trends are integrating machine learning in the form of neural networks to accelerate the IDA process, which usually either relies on analytic likelihoods, or resorts to slow, sequential MCMC samplers.…”

Section: Diagnostics and Fusion Data Streamsmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

View full text Add to dashboard Cite

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, plasmaprocessing 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. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

show abstract

“…This fusion workflow is just an example of the many fusion workflows that can benefit from increased computational power, including direct diagnostic data analysis, synthetic diagnostics, modeling/simulations, etc. Integrated data analysis 20 (IDA), for example, which can utilize multiple diagnostics to extract physics model parameters of interest (along with uncertainities), requires significant time for several synthetic diagnostics and the most accurate statistical inference methods [Markov-Chain Monte Carlo (MCMC)], presenting a challenge to process the data from long-pulse discharges.…”

Section: Federated Frameworkmentioning

confidence: 99%

A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

Churchill

Chang

Choi

et al. 2021

Fusion Science and Technology

View full text Add to dashboard Cite

The global nature of the ITER project along with its projected approximately petabyte-per-day data generation presents not only a unique challenge but also an opportunity for the fusion community to rethink, optimize, and enhance our scientific discovery process. Recognizing this, collaborative research with computational scientists was undertaken over the past several years to create a framework for large-scale data movement across wide-area networks to enable global near-real-time analysis of fusion data. This would broaden the available computational resources for analysis/simulation and increase the number of researchers actively participating in experiments.An official demonstration of this framework for fast, large data transfer and real-time analysis was carried out between the KSTAR tokamak in Daejeon, Korea, and Princeton Plasma Physics Laboratory (PPPL) in Princeton, New Jersey. Streaming large data transfer, with near-real-time movie creation and analysis of the KSTAR electron cyclotron emission imaging data, was performed using the Adaptable Input Output (I/O) System (ADIOS) framework, and comparisons were made at PPPL with simulation results from the XGC1 code. These demonstrations were made possible utilizing an optimized network configuration at PPPL, which achieved over 8.8 Gbps (88% utilization) in throughput tests from the National Fusion Research Institute to PPPL. This demonstration showed the feasibility for large-scale data analysis of KSTAR data and provides a nascent framework to enable use of globally distributed computational and personnel resources in pursuit of scientific knowledge from the ITER experiment.

show abstract

Integrated Data Analysis for Fusion: A Bayesian Tutorial for Fusion Diagnosticians

Cited by 7 publications

References 16 publications

Integrated data analysis on the electron temperature profile of HL-2A with the Bayesian probability inference method

Integrated data analysis on the electron temperature profile of HL-2A with the Bayesian probability inference method

2022 Review of Data-Driven Plasma Science

A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

Contact Info

Product

Resources

About