High-energy-density-physics measurements in implosions using Bayesian inference

Ruby, J. J.; Gaffney, Jim; Rygg, J. R.; Ping, Yuan; Collins, G. W.

doi:10.1063/5.0040616

Cited by 13 publications

(4 citation statements)

References 51 publications

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“…Recently, ICF and HEDP researchers have started to formalize the process by treating the physics as a second source of information and to build data-driven models that in are in some way informed by both sources. A variety of approaches have been explored; for example using simplified physics models [453][454][455][456], by using physics considerations to limit the size of the design space [457,458], or by attempting to combine data from disparate but physically related experiments [459]. Other important efforts aim to pose the benchmarking and tuning of large-scale multiphysics simulations as a Bayesian inference [397,460].…”

Section: Uncertainty Quantification and Bayesian Inferencementioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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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...

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Section: Uncertainty Quantification and Bayesian Inferencementioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It was emphasised in Kasim et al [2019] that using Bayes' Theorem was necessary to avoid underestimating uncertainty for the field of high energy density physics (of which ICF is a part). Gaffney et al [2019] pioneered the use of Bayesian calibration taking into account data from multiple NIF shots to make predictions on energy yield from future shots (see also Ruby et al [2021]). Finally Osthus et al [2019] illustrated the combination of multiple different possible extrapolation models to get more realistic uncertainties on predictions.…”

Section: Inertial Confinement Fusionmentioning

confidence: 99%

Quantification of Unknown Unknowns in Astronomy and Physics

Hatfield¹

2022

Preprint

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Uncertainty quantification is a key part of astronomy and physics; scientific researchers attempt to model both statistical and systematic uncertainties in their data as best as possible, often using a Bayesian framework. Decisions might then be made on the resulting uncertainty quantificationperhaps whether or not to believe in a certain theory, or whether to take certain actions. However it is well known that most statistical claims should be taken contextually; even if certain models are excluded at a very high degree of confidence, researchers are typically aware there may be systematics that were not accounted for, and thus typically will require confirmation from multiple independent sources before any novel results are truly accepted. In this paper we compare two methods in the astronomical literature that seek to attempt to quantify these 'unknown unknowns' -in particular attempting to produce realistic thick tails in the posterior of parameter estimation problems, that account for the possible existence of very large unknown effects. We test these methods on a series of case studies, and discuss how robust these methods would be in the presence of malicious interference with the scientific data.

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“…Bayesian inference techniques have been used to compare models to data obtained with multiple diagnostics. 32 Recently, Ruby et al 33 demonstrated a technique that utilized simultaneous measurements of spatially and temporally resolved x-ray self emission from the hotspot to constrain electron heat conduction transport models. Future work will utilize the Bayesian inference technique proposed in Ref.…”

Section: Simultaneous Xris and Emission-history Measurements Of Implo...mentioning

confidence: 99%

“…Future work will utilize the Bayesian inference technique proposed in Ref. 33 to study electron heat conduction with data simultaneously recorded with XRIS and PXTD.…”

Section: Simultaneous Xris and Emission-history Measurements Of Implo...mentioning

confidence: 99%

X-ray-imaging spectrometer (XRIS) for studies of residual kinetic energy and low-mode asymmetries in inertial confinement fusion implosions at OMEGA (invited)

Adrian

Bachmann

Betti

et al. 2022

Review of Scientific Instruments

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A system of x-ray imaging spectrometer (XRIS) has been implemented at the OMEGA Laser Facility and is capable of spatially and spectrally resolving x-ray self-emission from 5 to 40 keV. The system consists of three independent imagers with nearly orthogonal lines of sight for 3D reconstructions of the x-ray emission region. The distinct advantage of the XRIS system is its large dynamic range, which is enabled by the use of tantalum apertures with radii ranging from 50 μm to 1 mm, magnifications of 4 to 35×, and image plates with any filtration level. In addition, XRIS is capable of recording 1–100’s images along a single line of sight, facilitating advanced statistical inference on the detailed structure of the x-ray emitting regions. Properties such as P0 and P2 of an implosion are measured to 1% and 10% precision, respectively. Furthermore, T e can be determined with 5% accuracy.

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High-energy-density-physics measurements in implosions using Bayesian inference

Cited by 13 publications

References 51 publications

2022 Review of Data-Driven Plasma Science

2022 Review of Data-Driven Plasma Science

Quantification of Unknown Unknowns in Astronomy and Physics

X-ray-imaging spectrometer (XRIS) for studies of residual kinetic energy and low-mode asymmetries in inertial confinement fusion implosions at OMEGA (invited)

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