Advanced data analysis in inertial confinement fusion and high energy density physics

Knapp, Patrick F.; Lewis, William E.

doi:10.1063/5.0128661

Advanced data analysis in inertial confinement fusion and high energy density physics

Journal Article · Wed Jun 07 00:00:00 EDT 2023 · Review of Scientific Instruments

DOI:https://doi.org/10.1063/5.0128661· OSTI ID:1990135

^[1]; ^[2]

Sandia National Laboratory (SNL-NM), Albuquerque, NM (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sandia National Laboratory (SNL-NM), Albuquerque, NM (United States)

Bayesian analysis enables flexible and rigorous definition of statistical model assumptions with well-characterized propagation of uncertainties and resulting inferences for single-shot, repeated, or even cross-platform data. This approach has a strong history of application to a variety of problems in physical sciences ranging from inference of particle mass from multi-source high-energy particle data to analysis of black-hole characteristics from gravitational wave observations. The recent adoption of Bayesian statistics for analysis and design of high-energy density physics (HEDP) and inertial confinement fusion (ICF) experiments has provided invaluable gains in expert understanding and experiment performance. In this Review, we discuss the basic theory and practical application of the Bayesian statistics framework. We highlight a variety of studies from the HEDP and ICF literature, demonstrating the power of this technique. Due to the computational complexity of multi-physics models needed to analyze HEDP and ICF experiments, Bayesian inference is often not computationally tractable. Two sections are devoted to a review of statistical approximations, efficient inference algorithms, and data-driven methods, such as deep-learning and dimensionality reduction, which play a significant role in enabling use of the Bayesian framework. We provide additional discussion of various applications of Bayesian and machine learning methods that appear to be sparse in the HEDP and ICF literature constituting possible next steps for the community. We conclude by highlighting community needs, the resolution of which will improve trust in data-driven methods that have proven critical for accelerating the design and discovery cycle in many application areas.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA), Office of Defense Programs (DP)

Grant/Contract Number:: 89233218CNA000001; NA0003525

OSTI ID:: 1990135

Alternate ID(s):: OSTI ID: 2311460

Report Number(s):: LA-UR--23-22987; SAND--2023-05630J

Journal Information:: Review of Scientific Instruments, Journal Name: Review of Scientific Instruments Journal Issue: 6 Vol. 94; ISSN 0034-6748

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

References (240)

Nonlinear principal component analysis using autoassociative neural networks Kramer, Mark A. AIChE Journal, Vol. 37, Issue 2 https://doi.org/10.1002/aic.690370209	journal	February 1991
Data-driven projection method in fluid simulation Yang, Cheng; Yang, Xubo; Xiao, Xiangyun Computer Animation and Virtual Worlds, Vol. 27, Issue 3-4 https://doi.org/10.1002/cav.1695	journal	May 2016
Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values Paatero, Pentti; Tapper, Unto Environmetrics, Vol. 5, Issue 2 https://doi.org/10.1002/env.3170050203	journal	June 1994
Parameter inference with deep jointly informed neural networks Humbird, Kelli D.; Peterson, J. Luc; McClarren, Ryan G. Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 12, Issue 6 https://doi.org/10.1002/sam.11435	journal	July 2019
The Bayes Inference Engine Hanson, K. M.; Cunningham, G. S. Maximum Entropy and Bayesian Methods https://doi.org/10.1007/978-94-011-5430-7_15	book	January 1996
MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources Gorodetsky, A. A.; Jakeman, J. D.; Geraci, G. Computational Mechanics, Vol. 68, Issue 4 https://doi.org/10.1007/s00466-021-02042-0	journal	August 2021
Advancing Fusion with Machine Learning Research Needs Workshop Report Humphreys, David; Kupresanin, A.; Boyer, M. D. Journal of Fusion Energy, Vol. 39, Issue 4 https://doi.org/10.1007/s10894-020-00258-1	journal	August 2020
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC Vehtari, Aki; Gelman, Andrew; Gabry, Jonah Statistics and Computing, Vol. 27, Issue 5 https://doi.org/10.1007/s11222-016-9696-4	journal	August 2016
Nonlinear dimensionality reduction and data visualization: A review Yin, Hujun International Journal of Automation and Computing, Vol. 4, Issue 3 https://doi.org/10.1007/s11633-007-0294-y	journal	July 2007
Bayesian neural networks and density networks MacKay, David J. C. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 354, Issue 1 https://doi.org/10.1016/0168-9002(94)00931-7	journal	January 1995
Bayesian analysis of dichotomous quantal response models Zellner, Arnold; Rossi, Peter E. Journal of Econometrics, Vol. 25, Issue 3 https://doi.org/10.1016/0304-4076(84)90007-1	journal	July 1984
Tobit models: A survey Amemiya, Takeshi Journal of Econometrics, Vol. 24, Issue 1-2 https://doi.org/10.1016/0304-4076(84)90074-5	journal	January 1984
A calibration and data assimilation method using the Bayesian MARS emulator Stripling, H. F.; McClarren, R. G.; Kuranz, C. C. Annals of Nuclear Energy, Vol. 52 https://doi.org/10.1016/j.anucene.2012.08.025	journal	February 2013
Proper orthogonal decomposition closure models for turbulent flows: A numerical comparison Wang, Zhu; Akhtar, Imran; Borggaard, Jeff Computer Methods in Applied Mechanics and Engineering, Vol. 237-240 https://doi.org/10.1016/j.cma.2012.04.015	journal	September 2012
Data-driven operator inference for nonintrusive projection-based model reduction Peherstorfer, Benjamin; Willcox, Karen Computer Methods in Applied Mechanics and Engineering, Vol. 306 https://doi.org/10.1016/j.cma.2016.03.025	journal	July 2016
A physics-informed operator regression framework for extracting data-driven continuum models Patel, Ravi G.; Trask, Nathaniel A.; Wood, Mitchell A. Computer Methods in Applied Mechanics and Engineering, Vol. 373 https://doi.org/10.1016/j.cma.2020.113500	journal	January 2021
Keras2c: A library for converting Keras neural networks to real-time compatible C Conlin, Rory; Erickson, Keith; Abbate, Joseph Engineering Applications of Artificial Intelligence, Vol. 100 https://doi.org/10.1016/j.engappai.2021.104182	journal	April 2021
Using line intensity ratios to determine the geometry of plasma in stars via their apparent areas Hatfield, Peter High Energy Density Physics, Vol. 6, Issue 3 https://doi.org/10.1016/j.hedp.2009.10.001	journal	September 2010
Bayesian inference of inaccuracies in radiation transport physics from inertial confinement fusion experiments Gaffney, J. A.; Clark, D.; Sonnad, V. High Energy Density Physics, Vol. 9, Issue 3 https://doi.org/10.1016/j.hedp.2013.04.012	journal	September 2013
Principal component analysis of electron beams generated in K-shell aluminum X-pinch plasma produced by a compact LC-generator Yilmaz, M. F.; Danisman, Y.; Larour, J. High Energy Density Physics, Vol. 15 https://doi.org/10.1016/j.hedp.2015.03.010	journal	June 2015
Adjoint-based sensitivity analysis for high-energy density radiative transfer using flux-limited diffusion Humbird, Kelli D.; McClarren, Ryan G. High Energy Density Physics, Vol. 22 https://doi.org/10.1016/j.hedp.2016.12.002	journal	March 2017
The effects of magnetic field topology on secondary neutron spectra in Magnetized Liner Inertial Fusion Appelbe, B.; Pecover, J.; Chittenden, J. High Energy Density Physics, Vol. 22 https://doi.org/10.1016/j.hedp.2017.01.005	journal	March 2017
Bayesian inference of deceleration-phase Rayleigh-Taylor growth rates in laser-driven cylindrical implosions Tobias, B.; Kawaguchi, C. F.; Palaniyappan, S. High Energy Density Physics, Vol. 37 https://doi.org/10.1016/j.hedp.2020.100879	journal	November 2020
Review of the first charged-particle transport coefficient comparison workshop Grabowski, P. E.; Hansen, S. B.; Murillo, M. S. High Energy Density Physics, Vol. 37 https://doi.org/10.1016/j.hedp.2020.100905	journal	November 2020
Overview and comparative study of dimensionality reduction techniques for high dimensional data Ayesha, Shaeela; Hanif, Muhammad Kashif; Talib, Ramzan Information Fusion, Vol. 59 https://doi.org/10.1016/j.inffus.2020.01.005	journal	July 2020
A review of uncertainty quantification in deep learning: Techniques, applications and challenges Abdar, Moloud; Pourpanah, Farhad; Hussain, Sadiq Information Fusion, Vol. 76 https://doi.org/10.1016/j.inffus.2021.05.008	journal	December 2021
Machine learning strategies for systems with invariance properties Ling, Julia; Jones, Reese; Templeton, Jeremy Journal of Computational Physics, Vol. 318 https://doi.org/10.1016/j.jcp.2016.05.003	journal	August 2016
Hidden physics models: Machine learning of nonlinear partial differential equations Raissi, Maziar; Karniadakis, George Em Journal of Computational Physics, Vol. 357 https://doi.org/10.1016/j.jcp.2017.11.039	journal	March 2018
Conservative model reduction for finite-volume models Carlberg, Kevin; Choi, Youngsoo; Sargsyan, Syuzanna Journal of Computational Physics, Vol. 371 https://doi.org/10.1016/j.jcp.2018.05.019	journal	October 2018
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Raissi, M.; Perdikaris, P.; Karniadakis, G. E. Journal of Computational Physics, Vol. 378 https://doi.org/10.1016/j.jcp.2018.10.045	journal	February 2019
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data Zhu, Yinhao; Zabaras, Nicholas; Koutsourelakis, Phaedon-Stelios Journal of Computational Physics, Vol. 394 https://doi.org/10.1016/j.jcp.2019.05.024	journal	October 2019
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders Lee, Kookjin; Carlberg, Kevin T. Journal of Computational Physics https://doi.org/10.1016/j.jcp.2019.108973	journal	November 2019
Thermodynamically consistent physics-informed neural networks for hyperbolic systems Patel, Ravi G.; Manickam, Indu; Trask, Nathaniel A. Journal of Computational Physics, Vol. 449 https://doi.org/10.1016/j.jcp.2021.110754	journal	January 2022
Data-driven acceleration of thermal radiation transfer calculations with the dynamic mode decomposition and a sequential singular value decomposition McClarren, Ryan G.; Haut, Terry S. Journal of Computational Physics, Vol. 448 https://doi.org/10.1016/j.jcp.2021.110756	journal	January 2022
Local Lagrangian reduced-order modeling for the Rayleigh-Taylor instability by solution manifold decomposition Cheung, Siu Wun; Choi, Youngsoo; Copeland, Dylan Matthew Journal of Computational Physics, Vol. 472 https://doi.org/10.1016/j.jcp.2022.111655	journal	January 2023
A multi-output multi-fidelity Gaussian process model for non-hierarchical low-fidelity data fusion Lin, Quan; Qian, Jiachang; Cheng, Yuansheng Knowledge-Based Systems, Vol. 254 https://doi.org/10.1016/j.knosys.2022.109645	journal	October 2022
Bayesian model selection for group studies Stephan, Klaas Enno; Penny, Will D.; Daunizeau, Jean NeuroImage, Vol. 46, Issue 4 https://doi.org/10.1016/j.neuroimage.2009.03.025	journal	July 2009
Bayesian inference in FMRI Woolrich, Mark W. NeuroImage, Vol. 62, Issue 2 https://doi.org/10.1016/j.neuroimage.2011.10.047	journal	August 2012
A suite of neutron time-of-flight detectors to measure hot-spot motion in direct-drive inertial confinement fusion experiments on OMEGA Mannion, O. M.; Knauer, J. P.; Glebov, V. Yu. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 964 https://doi.org/10.1016/j.nima.2020.163774	journal	June 2020
A Bayesian tutorial for data assimilation Wikle, Christopher K.; Berliner, L. Mark Physica D: Nonlinear Phenomena, Vol. 230, Issue 1-2 https://doi.org/10.1016/j.physd.2006.09.017	journal	June 2007
A physics informed emulator for laser-driven radiating shock simulations McClarren, Ryan G.; Ryu, D.; Paul Drake, R. Reliability Engineering & System Safety, Vol. 96, Issue 9 https://doi.org/10.1016/j.ress.2010.08.012	journal	September 2011
Initial performance results of the OMEGA laser system Boehly, T. R.; Brown, D. L.; Craxton, R. S. Optics Communications, Vol. 133, Issue 1-6 https://doi.org/10.1016/s0030-4018(96)00325-2	journal	January 1997
Neural networks based subgrid scale modeling in large eddy simulations Sarghini, F.; de Felice, G.; Santini, S. Computers & Fluids, Vol. 32, Issue 1 https://doi.org/10.1016/s0045-7930(01)00098-6	journal	January 2003
Empirical Bayesian estimation of normal variances and covariances Champion, Colin J. Journal of Multivariate Analysis, Vol. 87, Issue 1 https://doi.org/10.1016/s0047-259x(02)00076-3	journal	October 2003
Machine-learning guided optimization of laser pulses for direct-drive implosions Wu, Fuyuan; Yang, Xiaohu; Ma, Yanyun High Power Laser Science and Engineering, Vol. 10 https://doi.org/10.1017/hpl.2022.4	journal	January 2022
An introduction to Bayesian inference in gravitational-wave astronomy: Parameter estimation, model selection, and hierarchical models Thrane, Eric; Talbot, Colm Publications of the Astronomical Society of Australia, Vol. 36 https://doi.org/10.1017/pasa.2019.2	journal	January 2019
Spectral analysis of nonlinear flows Rowley, Clarence W.; MeziĆ, Igor; Bagheri, Shervin Journal of Fluid Mechanics, Vol. 641 https://doi.org/10.1017/s0022112009992059	journal	November 2009
Statistical characterization of experimental magnetized liner inertial fusion stagnation images using deep-learning-based fuel–background segmentation Lewis, William E.; Knapp, Patrick F.; Harding, Eric C. Journal of Plasma Physics, Vol. 88, Issue 5 https://doi.org/10.1017/s0022377822000800	journal	September 2022
Optimizing the configuration of plasma radiation detectors in the presence of uncertain instrument response and inadequate physics Knapp, P. F.; Lewis, W. E.; Joseph, V. R. Journal of Plasma Physics, Vol. 89, Issue 1 https://doi.org/10.1017/s002237782200126x	journal	January 2023
Learning representations by back-propagating errors Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. Nature, Vol. 323, Issue 6088 https://doi.org/10.1038/323533a0	journal	October 1986
A higher-than-predicted measurement of iron opacity at solar interior temperatures Bailey, J. E.; Nagayama, T.; Loisel, G. P. Nature, Vol. 517, Issue 7532 https://doi.org/10.1038/nature14048	journal	December 2014
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
How high energy fluxes may affect Rayleigh–Taylor instability growth in young supernova remnants Kuranz, C. C.; Park, H. -S.; Huntington, C. M. Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-03548-7	journal	April 2018
Deep learning for universal linear embeddings of nonlinear dynamics Lusch, Bethany; Kutz, J. Nathan; Brunton, Steven L. Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-07210-0	journal	November 2018
Designing accurate emulators for scientific processes using calibration-driven deep models Thiagarajan, Jayaraman J.; Venkatesh, Bindya; Anirudh, Rushil Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19448-8	journal	November 2020
Automation and control of laser wakefield accelerators using Bayesian optimization Shalloo, R. J.; Dann, S. J. D.; Gruse, J. -N. Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-20245-6	journal	December 2020
Tripled yield in direct-drive laser fusion through statistical modelling Gopalaswamy, V.; Betti, R.; Knauer, J. P. Nature, Vol. 565, Issue 7741 https://doi.org/10.1038/s41586-019-0877-0	journal	January 2019
The data-driven future of high-energy-density physics Hatfield, Peter W.; Gaffney, Jim A.; Anderson, Gemma J. Nature, Vol. 593, Issue 7859 https://doi.org/10.1038/s41586-021-03382-w	journal	May 2021
Burning plasma achieved in inertial fusion Zylstra, A. B.; Hurricane, O. A.; Callahan, D. A. Nature, Vol. 601, Issue 7894 https://doi.org/10.1038/s41586-021-04281-w	journal	January 2022
Physics-informed machine learning Karniadakis, George Em; Kevrekidis, Ioannis G.; Lu, Lu Nature Reviews Physics, Vol. 3, Issue 6 https://doi.org/10.1038/s42254-021-00314-5	journal	May 2021
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators Lu, Lu; Jin, Pengzhan; Pang, Guofei Nature Machine Intelligence, Vol. 3, Issue 3 https://doi.org/10.1038/s42256-021-00302-5	journal	March 2021
Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider Govorkova, Ekaterina; Puljak, Ema; Aarrestad, Thea Nature Machine Intelligence, Vol. 4, Issue 2 https://doi.org/10.1038/s42256-022-00441-3	journal	February 2022
X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue Buchner, J.; Georgakakis, A.; Nandra, K. Astronomy & Astrophysics, Vol. 564 https://doi.org/10.1051/0004-6361/201322971	journal	April 2014
Estimating activity cycles with probabilistic methods Olspert, N.; Pelt, J.; Käpylä, M. J. Astronomy & Astrophysics, Vol. 615 https://doi.org/10.1051/0004-6361/201732524	journal	July 2018
The LCLS: A fourth generation light source using the SLAC linac Arthur, J.; Materlik, G.; Tatchyn, R. Review of Scientific Instruments, Vol. 66, Issue 2 https://doi.org/10.1063/1.1145778	journal	February 1995
Equation of State Calculations by Fast Computing Machines Metropolis, Nicholas; Rosenbluth, Arianna W.; Rosenbluth, Marshall N. The Journal of Chemical Physics, Vol. 21, Issue 6 https://doi.org/10.1063/1.1699114	journal	June 1953
Bayesian Adaptive Exploration Loredo, Thomas J. BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 23rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings https://doi.org/10.1063/1.1751377	conference	January 2004
Flat field anomalies in an x-ray charge coupled device camera measured using a Manson x-ray source Haugh, M. J.; Schneider, M. B. Review of Scientific Instruments, Vol. 79, Issue 10 https://doi.org/10.1063/1.2966457	journal	October 2008
Pulsed-power-driven cylindrical liner implosions of laser preheated fuel magnetized with an axial field Slutz, S. A.; Herrmann, M. C.; Vesey, R. A. Physics of Plasmas, Vol. 17, Issue 5 https://doi.org/10.1063/1.3333505	journal	May 2010
Thermonuclear ignition in inertial confinement fusion and comparison with magnetic confinement Betti, R.; Chang, P. Y.; Spears, B. K. Physics of Plasmas, Vol. 17, Issue 5 https://doi.org/10.1063/1.3380857	journal	May 2010
Measuring symmetry of implosions in cryogenic Hohlraums at the NIF using gated x-ray detectors (invited) Kyrala, G. A.; Dixit, S.; Glenzer, S. Review of Scientific Instruments, Vol. 81, Issue 10 https://doi.org/10.1063/1.3481028	journal	October 2010
High-resolution spectroscopy used to measure inertial confinement fusion neutron spectra on Omega (invited) Forrest, C. J.; Radha, P. B.; Glebov, V. Yu. Review of Scientific Instruments, Vol. 83, Issue 10 https://doi.org/10.1063/1.4742926	journal	October 2012
Simultaneous usage of pinhole and penumbral apertures for imaging small scale neutron sources from inertial confinement fusion experiments Guler, N.; Volegov, P.; Danly, C. R. Review of Scientific Instruments, Vol. 83, Issue 10 https://doi.org/10.1063/1.4746745	journal	October 2012
The magnetic recoil spectrometer for measurements of the absolute neutron spectrum at OMEGA and the NIF Casey, D. T.; Frenje, J. A.; Gatu Johnson, M. Review of Scientific Instruments, Vol. 84, Issue 4 https://doi.org/10.1063/1.4796042	journal	April 2013
Nuclear imaging of the fuel assembly in ignition experiments Grim, G. P.; Guler, N.; Merrill, F. E. Physics of Plasmas, Vol. 20, Issue 5 https://doi.org/10.1063/1.4807291	journal	May 2013
Flow strength of tantalum under ramp compression to 250 GPa Brown, J. L.; Alexander, C. S.; Asay, J. R. Journal of Applied Physics, Vol. 115, Issue 4 https://doi.org/10.1063/1.4863463	journal	January 2014
Neutron source reconstruction from pinhole imaging at National Ignition Facility Volegov, P.; Danly, C. R.; Fittinghoff, D. N. Review of Scientific Instruments, Vol. 85, Issue 2 https://doi.org/10.1063/1.4865456	journal	February 2014
Development of a Big Area BackLighter for high energy density experiments Flippo, K. A.; Kline, J. L.; Doss, F. W. Review of Scientific Instruments, Vol. 85, Issue 9 https://doi.org/10.1063/1.4893349	journal	September 2014
Investigation of ion kinetic effects in direct-drive exploding-pusher implosions at the NIF Rosenberg, M. J.; Zylstra, A. B.; Séguin, F. H. Physics of Plasmas, Vol. 21, Issue 12 https://doi.org/10.1063/1.4905064	journal	December 2014
A semi-analytic model of magnetized liner inertial fusion McBride, Ryan D.; Slutz, Stephen A. Physics of Plasmas, Vol. 22, Issue 5 https://doi.org/10.1063/1.4918953	journal	May 2015
Effects of magnetization on fusion product trapping and secondary neutron spectraa) Knapp, P. F.; Schmit, P. F.; Hansen, S. B. Physics of Plasmas, Vol. 22, Issue 5 https://doi.org/10.1063/1.4920948	journal	May 2015
Direct-drive inertial confinement fusion: A review Craxton, R. S.; Anderson, K. S.; Boehly, T. R. Physics of Plasmas, Vol. 22, Issue 11 https://doi.org/10.1063/1.4934714	journal	November 2015
Analysis of the neutron time-of-flight spectra from inertial confinement fusion experiments Hatarik, R.; Sayre, D. B.; Caggiano, J. A. Journal of Applied Physics, Vol. 118, Issue 18 https://doi.org/10.1063/1.4935455	journal	November 2015
Exploring magnetized liner inertial fusion with a semi-analytic model McBride, R. D.; Slutz, S. A.; Vesey, R. A. Physics of Plasmas, Vol. 23, Issue 1 https://doi.org/10.1063/1.4939479	journal	January 2016
Modeling of the L-shell copper X-pinch plasma produced by the compact generator of Ecole polytechnique using pattern recognition Larour, Jean; Aranchuk, Leonid E.; Danisman, Yusuf Physics of Plasmas, Vol. 23, Issue 3 https://doi.org/10.1063/1.4943874	journal	March 2016
Electron temperature measurements inside the ablating plasma of gas-filled hohlraums at the National Ignition Facility Barrios, M. A.; Liedahl, D. A.; Schneider, M. B. Physics of Plasmas, Vol. 23, Issue 5 https://doi.org/10.1063/1.4948276	journal	April 2016
A high-resolving-power x-ray spectrometer for the OMEGA EP Laser (invited) Nilson, P. M.; Ehrne, F.; Mileham, C. Review of Scientific Instruments, Vol. 87, Issue 11 https://doi.org/10.1063/1.4961076	journal	August 2016
Uncertainty analysis of signal deconvolution using a measured instrument response function Hartouni, E. P.; Beeman, B.; Caggiano, J. A. Review of Scientific Instruments, Vol. 87, Issue 11 https://doi.org/10.1063/1.4963867	journal	October 2016
PC spectra analysis of L-shell copper X-pinch plasma produced by the compact generator of Ecole polytechnique Yilmaz, M. F.; Danisman, Y.; Larour, J. AIP Conference Proceedings https://doi.org/10.1063/1.4975726	conference	January 2017
Zonal flow generation in inertial confinement fusion implosions Peterson, J. L.; Humbird, K. D.; Field, J. E. Physics of Plasmas, Vol. 24, Issue 3 https://doi.org/10.1063/1.4977912	journal	March 2017
Development of an inertial confinement fusion platform to study charged-particle-producing nuclear reactions relevant to nuclear astrophysics Gatu Johnson, M.; Zylstra, A. B.; Bacher, A. Physics of Plasmas, Vol. 24, Issue 4 https://doi.org/10.1063/1.4979186	journal	April 2017
An iterative forward analysis technique to determine the equation of state of dynamically compressed materials Ali, S. J.; Kraus, R. G.; Fratanduono, D. E. Journal of Applied Physics, Vol. 121, Issue 19 https://doi.org/10.1063/1.4983067	journal	May 2017
Three-dimensional reconstruction of neutron, gamma-ray, and x-ray sources using spherical harmonic decomposition Volegov, P. L.; Danly, C. R.; Fittinghoff, D. Journal of Applied Physics, Vol. 122, Issue 17 https://doi.org/10.1063/1.4986652	journal	November 2017
The physics of long- and intermediate-wavelength asymmetries of the hot spot: Compression hydrodynamics and energetics Bose, A.; Betti, R.; Shvarts, D. Physics of Plasmas, Vol. 24, Issue 10 https://doi.org/10.1063/1.4995250	journal	October 2017
Deep learning: A guide for practitioners in the physical sciences Spears, Brian K.; Brase, James; Bremer, Peer-Timo Physics of Plasmas, Vol. 25, Issue 8 https://doi.org/10.1063/1.5020791	journal	August 2018
A Wolter imager on the Z machine to diagnose warm x-ray sources Fein, J. R.; Ampleford, D. J.; Vogel, J. K. Review of Scientific Instruments, Vol. 89, Issue 10 https://doi.org/10.1063/1.5038347	journal	October 2018
Using multiple neutron time of flight detectors to determine the hot spot velocity Hatarik, R.; Nora, R. C.; Spears, B. K. Review of Scientific Instruments, Vol. 89, Issue 10 https://doi.org/10.1063/1.5039372	journal	October 2018
Origins and effects of mix on magnetized liner inertial fusion target performance Knapp, P. F.; Gomez, M. R.; Hansen, S. B. Physics of Plasmas, Vol. 26, Issue 1 https://doi.org/10.1063/1.5064548	journal	January 2019
Investigation of electron beam effects on L-shell Mo plasma produced by a compact LC generator using pattern recognition Yilmaz, M. F.; Danisman, Y.; Ozdemir, M. Matter and Radiation at Extremes, Vol. 4, Issue 2 https://doi.org/10.1063/1.5081676	journal	March 2019
Constraining preheat energy deposition in MagLIF experiments with multi-frame shadowgraphy Harvey-Thompson, A. J.; Geissel, M.; Jennings, C. A. Physics of Plasmas, Vol. 26, Issue 3 https://doi.org/10.1063/1.5086044	journal	March 2019
A semi-analytic model of gas-puff liner-on-target magneto-inertial fusion Narkis, J.; Rahman, H. U.; Valenzuela, J. C. Physics of Plasmas, Vol. 26, Issue 3 https://doi.org/10.1063/1.5086056	journal	March 2019
Turbulent mixing and transition criteria of flows induced by hydrodynamic instabilities Zhou, Ye; Clark, Timothy T.; Clark, Daniel S. Physics of Plasmas, Vol. 26, Issue 8 https://doi.org/10.1063/1.5088745	journal	August 2019
The blind implosion-maker: Automated inertial confinement fusion experiment design Hatfield, P. W.; Rose, S. J.; Scott, R. H. H. Physics of Plasmas, Vol. 26, Issue 6 https://doi.org/10.1063/1.5091985	journal	June 2019
Making inertial confinement fusion models more predictive Gaffney, Jim A.; Brandon, Scott T.; Humbird, Kelli D. Physics of Plasmas, Vol. 26, Issue 8 https://doi.org/10.1063/1.5108667	journal	August 2019
Inverse problem instabilities in large-scale modeling of matter in extreme conditions Kasim, M. F.; Galligan, T. P.; Topp-Mugglestone, J. Physics of Plasmas, Vol. 26, Issue 11 https://doi.org/10.1063/1.5125979	journal	November 2019
Analysis of NIF scaling using physics informed machine learning Hsu, Abigail; Cheng, Baolian; Bradley, Paul A. Physics of Plasmas, Vol. 27, Issue 1 https://doi.org/10.1063/1.5130585	journal	January 2020
Characterizing magnetized plasmas with dynamic mode decomposition Kaptanoglu, A. A.; Morgan, K. D.; Hansen, C. J. Physics of Plasmas, Vol. 27, Issue 3 https://doi.org/10.1063/1.5138932	journal	March 2020
An analytic model for laser-driven ablative implosion of spherical shell targets Ahlborn, Boye Physics of Fluids, Vol. 25, Issue 3 https://doi.org/10.1063/1.863769	journal	January 1982
Development of the indirect‐drive approach to inertial confinement fusion and the target physics basis for ignition and gain Lindl, John Physics of Plasmas, Vol. 2, Issue 11 https://doi.org/10.1063/1.871025	journal	November 1995
Richtmyer–Meshkov instability with strong radiatively driven shocks Dimonte, Guy; Frerking, C. Eric; Schneider, Marilyn Physics of Plasmas, Vol. 3, Issue 2 https://doi.org/10.1063/1.871889	journal	February 1996
Three‐dimensional simulations of Nova high growth factor capsule implosion experiments Marinak, M. M.; Tipton, R. E.; Landen, O. L. Physics of Plasmas, Vol. 3, Issue 5 https://doi.org/10.1063/1.872004	journal	May 1996
Turbulent Richtmyer–Meshkov instability experiments with strong radiatively driven shocks Dimonte, Guy; Schneider, Marilyn Physics of Plasmas, Vol. 4, Issue 12 https://doi.org/10.1063/1.872597	journal	December 1997
Deep learning for NLTE spectral opacities Kluth, G.; Humbird, K. D.; Spears, B. K. Physics of Plasmas, Vol. 27, Issue 5 https://doi.org/10.1063/5.0006784	journal	May 2020
Quantification of MagLIF morphology using the Mallat scattering transformation Glinsky, Michael E.; Moore, Thomas W.; Lewis, William E. Physics of Plasmas, Vol. 27, Issue 11 https://doi.org/10.1063/5.0010781	journal	November 2020
A novel, magnetically driven convergent Richtmyer–Meshkov platform Knapp, P. F.; Martin, M. R.; Yager-Elorriaga, D. Physics of Plasmas, Vol. 27, Issue 9 https://doi.org/10.1063/5.0013194	journal	September 2020
High-energy-density-physics measurements in implosions using Bayesian inference Ruby, J. J.; Gaffney, J. A.; Rygg, J. R. Physics of Plasmas, Vol. 28, Issue 3 https://doi.org/10.1063/5.0040616	journal	March 2021
Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data Humbird, K. D.; Peterson, J. L.; Salmonson, J. Physics of Plasmas, Vol. 28, Issue 4 https://doi.org/10.1063/5.0041907	journal	April 2021
Diagnosing plasma magnetization in inertial confinement fusion implosions using secondary deuterium-tritium reactions Sio, H.; Moody, J. D.; Ho, D. D. Review of Scientific Instruments, Vol. 92, Issue 4 https://doi.org/10.1063/5.0043381	journal	April 2021
Neural network for 3D inertial confinement fusion shell reconstruction from single radiographs Wolfe, Bradley T.; Han, Zhizhong; Ben-Benjamin, Jonathan S. Review of Scientific Instruments, Vol. 92, Issue 3 https://doi.org/10.1063/5.0043653	journal	March 2021
Measurements of electron temperature in high-energy-density plasmas using gated x-ray pinhole imaging Schaeffer, D. B.; Fox, W.; Rosenberg, M. J. Review of Scientific Instruments, Vol. 92, Issue 4 https://doi.org/10.1063/5.0043833	journal	April 2021
Development of a deep learning based automated data analysis for step-filter x-ray spectrometers in support of high-repetition rate short-pulse laser-driven acceleration experiments Simpson, R. A.; Mariscal, D.; Williams, G. J. Review of Scientific Instruments, Vol. 92, Issue 7 https://doi.org/10.1063/5.0043835	journal	July 2021
Efficient model for electronic transport in high energy-density matter Stanton, Liam G.; Murillo, Michael S. Physics of Plasmas, Vol. 28, Issue 8 https://doi.org/10.1063/5.0048162	journal	August 2021
Simultaneous inference of the compressibility and inelastic response of tantalum under extreme loading Schill, W. J.; Austin, R. A.; Schimdt, K. L. Journal of Applied Physics, Vol. 130, Issue 5 https://doi.org/10.1063/5.0056437	journal	August 2021
Deep-learning-enabled Bayesian inference of fuel magnetization in magnetized liner inertial fusion Lewis, William E.; Knapp, Patrick F.; Slutz, Stephen A. Physics of Plasmas, Vol. 28, Issue 9 https://doi.org/10.1063/5.0056749	journal	September 2021
Background measurement methods for opacity experiments conducted at the Z facility Dunham, G. S.; Nagayama, T.; Bailey, J. E. Review of Scientific Instruments, Vol. 92, Issue 8 https://doi.org/10.1063/5.0057225	journal	August 2021
Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules Vazirani, N. N.; Grosskopf, M. J.; Stark, D. J. Physics of Plasmas, Vol. 28, Issue 12 https://doi.org/10.1063/5.0063745	journal	December 2021
Model validation for inferred hot-spot conditions in National Ignition Facility experiments Zylstra, A. B.; Nora, R.; Patel, P. Physics of Plasmas, Vol. 28, Issue 12 https://doi.org/10.1063/5.0069366	journal	December 2021
Estimation of stagnation performance metrics in magnetized liner inertial fusion experiments using Bayesian data assimilation Knapp, P. F.; Glinsky, M. E.; Schaeuble, M. A. Physics of Plasmas, Vol. 29, Issue 5 https://doi.org/10.1063/5.0087115	journal	May 2022
Density reconstruction in convergent high-energy-density systems using x-ray radiography and Bayesian inference Ressel, S.; Ruby, J. J.; Collins, G. W. Physics of Plasmas, Vol. 29, Issue 7 https://doi.org/10.1063/5.0094729	journal	July 2022
Reduced scale stripline platform to extend accessible pressures on the Z machine: Shockless compression of platinum to 650 GPa Porwitzky, Andrew; Brown, Justin; Duwal, Sakun Journal of Applied Physics, Vol. 132, Issue 11 https://doi.org/10.1063/5.0095590	journal	September 2022
Three-dimensional electron temperature measurement of inertial confinement fusion hotspots using x-ray emission tomography Wong, Ka Wai; Bachmann, Benjamin Review of Scientific Instruments, Vol. 93, Issue 7 https://doi.org/10.1063/5.0097471	journal	July 2022
Quantifying electron temperature distributions from time-integrated x-ray emission spectra MacDonald, M. J.; Liedahl, D. A.; Brown, G. V. Review of Scientific Instruments, Vol. 93, Issue 9 https://doi.org/10.1063/5.0101571	journal	September 2022
Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction Wolfe, Bradley T.; Falato, Michael J.; Zhang, Xinhua Review of Scientific Instruments, Vol. 94, Issue 2 https://doi.org/10.1063/5.0101681	journal	February 2023
Discovering governing equations from data by sparse identification of nonlinear dynamical systems Brunton, Steven L.; Proctor, Joshua L.; Kutz, J. Nathan Proceedings of the National Academy of Sciences, Vol. 113, Issue 15 https://doi.org/10.1073/pnas.1517384113	journal	March 2016
Supernova, nuclear synthesis, fluid instabilities, and interfacial mixing Abarzhi, Snezhana I.; Bhowmick, Aklant K.; Naveh, Annie Proceedings of the National Academy of Sciences, Vol. 116, Issue 37 https://doi.org/10.1073/pnas.1714502115	journal	November 2018
Learning data-driven discretizations for partial differential equations Bar-Sinai, Yohai; Hoyer, Stephan; Hickey, Jason Proceedings of the National Academy of Sciences, Vol. 116, Issue 31 https://doi.org/10.1073/pnas.1814058116	journal	July 2019
Improved surrogates in inertial confinement fusion with manifold and cycle consistencies Anirudh, Rushil; Thiagarajan, Jayaraman J.; Bremer, Peer-Timo Proceedings of the National Academy of Sciences, Vol. 117, Issue 18 https://doi.org/10.1073/pnas.1916634117	journal	April 2020
Machine learning–accelerated computational fluid dynamics Kochkov, Dmitrii; Smith, Jamie A.; Alieva, Ayya Proceedings of the National Academy of Sciences, Vol. 118, Issue 21 https://doi.org/10.1073/pnas.2101784118	journal	May 2021
Bayes in the sky: Bayesian inference and model selection in cosmology Trotta, Roberto Contemporary Physics, Vol. 49, Issue 2 https://doi.org/10.1080/00107510802066753	journal	March 2008
Accurate Approximations for Posterior Moments and Marginal Densities Tierney, Luke; Kadane, Joseph B. Journal of the American Statistical Association, Vol. 81, Issue 393 https://doi.org/10.1080/01621459.1986.10478240	journal	March 1986
A Forward Analytic Model of Neutron Time-of-Flight Signals for Inferring Ion Temperatures from MagLIF Experiments Weaver, Colin; Cooper, Gary; Perfetti, Christopher Fusion Science and Technology, Vol. 78, Issue 2 https://doi.org/10.1080/15361055.2021.1961540	journal	January 2022
WFPC2 Studies of the Crab Nebula. III. Magnetic Rayleigh-Taylor Instabilities and the Origin of the Filaments Hester, J. Jeff; Stone, James M.; Scowen, Paul A. The Astrophysical Journal, Vol. 456 https://doi.org/10.1086/176643	journal	January 1996
`emcee` : The MCMC Hammer Foreman-Mackey, Daniel; Hogg, David W.; Lang, Dustin Publications of the Astronomical Society of the Pacific, Vol. 125, Issue 925 https://doi.org/10.1086/670067	journal	March 2013
LABORATORY MEASUREMENTS OF WHITE DWARF PHOTOSPHERIC SPECTRAL LINES: H β Falcon, Ross E.; Rochau, G. A.; Bailey, J. E. The Astrophysical Journal, Vol. 806, Issue 2 https://doi.org/10.1088/0004-637x/806/2/214	journal	June 2015
Maximum entropy based reconstruction of soft X-ray emissivity profiles in W7-AS Ertl, K.; Linden, W. von der; Dose, V. Nuclear Fusion, Vol. 36, Issue 11 https://doi.org/10.1088/0029-5515/36/11/i03	journal	November 1996
Relativistic calculation of fusion product spectra for thermonuclear plasmas Ballabio, L.; Källne, J.; Gorini, G. Nuclear Fusion, Vol. 38, Issue 11 https://doi.org/10.1088/0029-5515/38/11/310	journal	November 1998
Diagnosing implosion performance at the National Ignition Facility (NIF) by means of neutron spectrometry Frenje, J. A.; Bionta, R.; Bond, E. J. Nuclear Fusion, Vol. 53, Issue 4 https://doi.org/10.1088/0029-5515/53/4/043014	journal	March 2013
Development of a Bayesian method for the analysis of inertial confinement fusion experiments on the NIF Gaffney, J. A.; Clark, D.; Sonnad, V. Nuclear Fusion, Vol. 53, Issue 7 https://doi.org/10.1088/0029-5515/53/7/073032	journal	June 2013
Fusion neutron energies and spectra Brysk, H. Plasma Physics, Vol. 15, Issue 7 https://doi.org/10.1088/0032-1028/15/7/001	journal	July 1973
Current tomography for axisymmetric plasmas Svensson, J.; Werner, A. Plasma Physics and Controlled Fusion, Vol. 50, Issue 8 https://doi.org/10.1088/0741-3335/50/8/085002	journal	May 2008
Universal features of the equation of state of solids Vinet, P.; Rose, J. H.; Ferrante, J. Journal of Physics: Condensed Matter, Vol. 1, Issue 11 https://doi.org/10.1088/0953-8984/1/11/002	journal	March 1989
Introduction to spectral line shape theory Gomez, T. A.; Nagayama, T.; Cho, P. B. Journal of Physics B: Atomic, Molecular and Optical Physics, Vol. 55, Issue 3 https://doi.org/10.1088/1361-6455/ac4f31	journal	February 2022
Data-driven profile prediction for DIII-D Abbate, J.; Conlin, R.; Kolemen, E. Nuclear Fusion, Vol. 61, Issue 4 https://doi.org/10.1088/1741-4326/abe08d	journal	March 2021
An overview of magneto-inertial fusion on the Z machine at Sandia National Laboratories Yager-Elorriaga, D. A.; Gomez, M. R.; Ruiz, D. E. Nuclear Fusion, Vol. 62, Issue 4 https://doi.org/10.1088/1741-4326/ac2dbe	journal	February 2022
Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks Jalalvand, Azarakhsh; Kaptanoglu, Alan A.; Garcia, Alvin V. Nuclear Fusion, Vol. 62, Issue 2 https://doi.org/10.1088/1741-4326/ac3be7	journal	December 2021
Constraints on ion velocity distributions from fusion product spectroscopy Crilly, A. J.; Appelbe, B. D.; Mannion, O. M. Nuclear Fusion, Vol. 62, Issue 12 https://doi.org/10.1088/1741-4326/ac90d5	journal	October 2022
The Laser Mega-Joule : LMJ & PETAL status and Program Overview Miquel, J-L; Lion, C.; Vivini, P. Journal of Physics: Conference Series, Vol. 688 https://doi.org/10.1088/1742-6596/688/1/012067	journal	March 2016
The Laser-Driven X-ray Big Area Backlighter (BABL): Design, Optimization, and Evolution Flippo, Kirk; DeVolder, Barbara; Doss, Forrest Journal of Physics: Conference Series, Vol. 717 https://doi.org/10.1088/1742-6596/717/1/012062	journal	May 2016
Fast inference of deep neural networks in FPGAs for particle physics Duarte, J.; Han, S.; Harris, P. Journal of Instrumentation, Vol. 13, Issue 07 https://doi.org/10.1088/1748-0221/13/07/p07027	journal	July 2018
Fast inference of Boosted Decision Trees in FPGAs for particle physics Summers, S.; Guglielmo, G. Di; Duarte, J. Journal of Instrumentation, Vol. 15, Issue 05 https://doi.org/10.1088/1748-0221/15/05/p05026	journal	May 2020
Compressing deep neural networks on FPGAs to binary and ternary precision with `hls4ml` Ngadiuba, Jennifer; Loncar, Vladimir; Pierini, Maurizio Machine Learning: Science and Technology, Vol. 2, Issue 1 https://doi.org/10.1088/2632-2153/aba042	journal	December 2020
Suppressing simulation bias in multi-modal data using transfer learning Kustowski, Bogdan; Gaffney, Jim A.; Spears, Brian K. Machine Learning: Science and Technology, Vol. 3, Issue 1 https://doi.org/10.1088/2632-2153/ac5e3e	journal	March 2022
Testing the manifold hypothesis Fefferman, Charles; Mitter, Sanjoy; Narayanan, Hariharan Journal of the American Mathematical Society, Vol. 29, Issue 4 https://doi.org/10.1090/jams/852	journal	February 2016
Bayesian Inference of Signaling Network Topology in a Cancer Cell Line Hill, Steven M.; Lu, Yiling; Molina, Jennifer Bioinformatics, Vol. 28, Issue 21 https://doi.org/10.1093/bioinformatics/bts514	journal	August 2012
Sparsely sampling the sky: a Bayesian experimental design approach Paykari, P.; Jaffe, A. H. Monthly Notices of the Royal Astronomical Society, Vol. 433, Issue 4 https://doi.org/10.1093/mnras/stt982	journal	June 2013
Rayleigh–Taylor instability in magnetohydrodynamic simulations of the Crab nebula Porth, Oliver; Komissarov, Serguei S.; Keppens, Rony Monthly Notices of the Royal Astronomical Society, Vol. 443, Issue 1 https://doi.org/10.1093/mnras/stu1082	journal	July 2014
Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling Perdikaris, P.; Raissi, M.; Damianou, A. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 473, Issue 2198 https://doi.org/10.1098/rspa.2016.0751	journal	February 2017
Analysing brain networks in population neuroscience: a case for the Bayesian philosophy Bzdok, Danilo; Floris, Dorothea L.; Marquand, Andre F. Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 375, Issue 1796 https://doi.org/10.1098/rstb.2019.0661	journal	February 2020
Observation of Gravitational Waves from a Binary Black Hole Merger Abbott, B. P.; Abbott, R.; Abbott, T. D. Physical Review Letters, Vol. 116, Issue 6 https://doi.org/10.1103/PhysRevLett.116.061102	journal	February 2016
Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster St. John, Jason; Herwig, Christian; Kafkes, Diana Physical Review Accelerators and Beams, Vol. 24, Issue 10 https://doi.org/10.1103/physrevaccelbeams.24.104601	journal	October 2021
Artificial neural network subgrid models of 2D compressible magnetohydrodynamic turbulence Rosofsky, Shawn G.; Huerta, E. A. Physical Review D, Vol. 101, Issue 8 https://doi.org/10.1103/physrevd.101.084024	journal	April 2020
Bayesian model selection analysis of WMAP3 Parkinson, David; Mukherjee, Pia; Liddle, Andrew R. Physical Review D, Vol. 73, Issue 12 https://doi.org/10.1103/physrevd.73.123523	journal	June 2006
Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library Veitch, J.; Raymond, V.; Farr, B. Physical Review D, Vol. 91, Issue 4 https://doi.org/10.1103/physrevd.91.042003	journal	February 2015
Template-based gravitational-wave echoes search using Bayesian model selection Lo, Rico K. L.; Li, Tjonnie G. F.; Weinstein, Alan J. Physical Review D, Vol. 99, Issue 8 https://doi.org/10.1103/physrevd.99.084052	journal	April 2019
Constraining physical models at gigabar pressures Ruby, J. J.; Rygg, J. R.; Chin, D. A. Physical Review E, Vol. 102, Issue 5 https://doi.org/10.1103/physreve.102.053210	journal	November 2020
Physics-constrained, low-dimensional models for magnetohydrodynamics: First-principles and data-driven approaches Kaptanoglu, Alan A.; Morgan, Kyle D.; Hansen, Chris J. Physical Review E, Vol. 104, Issue 1 https://doi.org/10.1103/physreve.104.015206	journal	July 2021
Multifidelity regression of sparse plasma transport data available in disparate physical regimes Stanek, Lucas J.; Bopardikar, Shaunak D.; Murillo, Michael S. Physical Review E, Vol. 104, Issue 6 https://doi.org/10.1103/physreve.104.065303	journal	December 2021
Measurements of the temperature and velocity of the dense fuel layer in inertial confinement fusion experiments Mannion, O. M.; Crilly, A. J.; Forrest, C. J. Physical Review E, Vol. 105, Issue 5 https://doi.org/10.1103/physreve.105.055205	journal	May 2022
Nonlinear evolution of an interface in the Richtmyer-Meshkov instability Matsuoka, Chihiro; Nishihara, Katsunobu; Fukuda, Yuko Physical Review E, Vol. 67, Issue 3 https://doi.org/10.1103/physreve.67.036301	journal	March 2003
Solution to Rayleigh-Taylor instabilities: Bubbles, spikes, and their scalings Mikaelian, Karnig O. Physical Review E, Vol. 89, Issue 5 https://doi.org/10.1103/physreve.89.053009	journal	May 2014
Measurements of Magneto-Rayleigh-Taylor Instability Growth during the Implosion of Initially Solid Al Tubes Driven by the 20-MA, 100-ns Z Facility Sinars, D. B.; Slutz, S. A.; Herrmann, M. C. Physical Review Letters, Vol. 105, Issue 18 https://doi.org/10.1103/physrevlett.105.185001	journal	October 2010
Experimental Demonstration of Fusion-Relevant Conditions in Magnetized Liner Inertial Fusion Gomez, M. R.; Slutz, S. A.; Sefkow, A. B. Physical Review Letters, Vol. 113, Issue 15 https://doi.org/10.1103/physrevlett.113.155003	journal	October 2014
Understanding Fuel Magnetization and Mix Using Secondary Nuclear Reactions in Magneto-Inertial Fusion Schmit, P. F.; Knapp, P. F.; Hansen, S. B. Physical Review Letters, Vol. 113, Issue 15 https://doi.org/10.1103/physrevlett.113.155004	journal	October 2014
Systematic Study of L -Shell Opacity at Stellar Interior Temperatures Nagayama, T.; Bailey, J. E.; Loisel, G. P. Physical Review Letters, Vol. 122, Issue 23 https://doi.org/10.1103/physrevlett.122.235001	journal	June 2019
Demonstration of Scale-Invariant Rayleigh-Taylor Instability Growth in Laser-Driven Cylindrical Implosion Experiments Sauppe, J. P.; Palaniyappan, S.; Tobias, B. J. Physical Review Letters, Vol. 124, Issue 18 https://doi.org/10.1103/physrevlett.124.185003	journal	May 2020
Energy Flow in Thin Shell Implosions and Explosions Ruby, J. J.; Rygg, J. R.; Chin, D. A. Physical Review Letters, Vol. 125, Issue 21 https://doi.org/10.1103/physrevlett.125.215001	journal	November 2020
Bayesian Optimization of a Laser-Plasma Accelerator Jalas, Sören; Kirchen, Manuel; Messner, Philipp Physical Review Letters, Vol. 126, Issue 10 https://doi.org/10.1103/physrevlett.126.104801	journal	March 2021
Harmonic Generation and Inverse Cascade in the z-Pinch Driven, Preseeded Multimode, Magneto-Rayleigh-Taylor Instability Ruiz, D. E.; Yager-Elorriaga, D. A.; Peterson, K. J. Physical Review Letters, Vol. 128, Issue 25 https://doi.org/10.1103/physrevlett.128.255001	journal	June 2022
Bayesian inference in physics von Toussaint, Udo Reviews of Modern Physics, Vol. 83, Issue 3 https://doi.org/10.1103/revmodphys.83.943	journal	September 2011
Machine learning and the physical sciences Carleo, Giuseppe; Cirac, Ignacio; Cranmer, Kyle Reviews of Modern Physics, Vol. 91, Issue 4 https://doi.org/10.1103/revmodphys.91.045002	journal	December 2019
Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): applications to tomography Fessler, J. A. IEEE Transactions on Image Processing, Vol. 5, Issue 3 https://doi.org/10.1109/83.491322	journal	March 1996
Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures No authors listed IEEE Signal Processing Magazine, Vol. 26, Issue 1 https://doi.org/10.1109/msp.2008.930649	journal	January 2009
Deep Neural Network Initialization With Decision Trees Humbird, Kelli D.; Peterson, J. Luc; Mcclarren, Ryan G. IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, Issue 5 https://doi.org/10.1109/tnnls.2018.2869694	journal	May 2019
Real-Time and Adaptive Reservoir Computing With Application to Profile Prediction in Fusion Plasma Jalalvand, Azarakhsh; Abbate, Joseph; Conlin, Rory IEEE Transactions on Neural Networks and Learning Systems, Vol. 33, Issue 6 https://doi.org/10.1109/tnnls.2021.3085504	journal	June 2022
Invariant Scattering Convolution Networks Bruna, Joan; Mallat, S. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Issue 8 https://doi.org/10.1109/tpami.2012.230	journal	August 2013
Using Sparse Gaussian Processes for Predicting Robust Inertial Confinement Fusion Implosion Yields Hatfield, Peter; Rose, Steven; Scott, Robbie IEEE Transactions on Plasma Science https://doi.org/10.1109/tps.2019.2944416	journal	January 2019
Transfer Learning as a Tool for Reducing Simulation Bias: Application to Inertial Confinement Fusion Kustowski, Bogdan; Gaffney, Jim A.; Spears, Brian K. IEEE Transactions on Plasma Science, Vol. 48, Issue 1 https://doi.org/10.1109/tps.2019.2948339	journal	January 2020
Transfer Learning to Model Inertial Confinement Fusion Experiments Humbird, K. D.; Peterson, J. L.; Spears, B. K. IEEE Transactions on Plasma Science, Vol. 48, Issue 1 https://doi.org/10.1109/tps.2019.2955098	journal	January 2020
Identifying Entangled Physics Relationships Through Sparse Matrix Decomposition to Inform Plasma Fusion Design Fernandez-Godino, M. Giselle; Grosskopf, Michael J.; Nakhleh, Julia B. IEEE Transactions on Plasma Science, Vol. 49, Issue 8 https://doi.org/10.1109/tps.2021.3098482	journal	August 2021
Mode Jumping Proposals in MCMC Tjelmeland, Hakon; Hegstad, Bjorn Kare Scandinavian Journal of Statistics, Vol. 28, Issue 1 https://doi.org/10.1111/1467-9469.00232	journal	March 2001
Probabilistic Principal Component Analysis Tipping, Michael E.; Bishop, Christopher M. Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 61, Issue 3, p. 611-622 https://doi.org/10.1111/1467-9868.00196	journal	August 1999
Bayesian calibration of computer models Kennedy, Marc C.; O'Hagan, Anthony Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3 https://doi.org/10.1111/1467-9868.00294	journal	August 2001
Bayesian inference, Gibbs' sampler and uncertainty estimation in geophysical inversion1 Sen, Mrinal K.; Stoffa, Paul L. Geophysical Prospecting, Vol. 44, Issue 2 https://doi.org/10.1111/j.1365-2478.1996.tb00152.x	journal	March 1996
On model selection forecasting, dark energy and modified gravity: On model selection, dark energy and gravity Heavens, A. F.; Kitching, T. D.; Verde, L. Monthly Notices of the Royal Astronomical Society, Vol. 380, Issue 3 https://doi.org/10.1111/j.1365-2966.2007.12134.x	journal	August 2007
MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics Feroz, F.; Hobson, M. P.; Bridges, M. Monthly Notices of the Royal Astronomical Society, Vol. 398, Issue 4 https://doi.org/10.1111/j.1365-2966.2009.14548.x	journal	October 2009
Estimating material properties under extreme conditions by using Bayesian model calibration with functional outputs Brown, J. L.; Hund, L. B. Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 67, Issue 4 https://doi.org/10.1111/rssc.12273	journal	March 2018
The National Ignition Facility Miller, George H. Optical Engineering, Vol. 43, Issue 12 https://doi.org/10.1117/1.1814767	journal	December 2004
ELI-Beamlines laser systems: status and design options Rus, B.; Bakule, P.; Kramer, D. High-Power, High-Energy, and High-Intensity Laser Technology; and Research Using Extreme Light: Entering New Frontiers with Petawatt-Class Lasers https://doi.org/10.1117/12.2021264	conference	May 2013
Image reconstruction with uncertainty quantification in photoacoustic tomography Tick, Jenni; Pulkkinen, Aki; Tarvainen, Tanja The Journal of the Acoustical Society of America, Vol. 139, Issue 4 https://doi.org/10.1121/1.4945990	journal	April 2016
Bayesian Inference of Phylogeny and Its Impact on Evolutionary Biology Huelsenbeck, J. P. Science, Vol. 294, Issue 5550 https://doi.org/10.1126/science.1065889	journal	December 2001
Nonlinear Dimensionality Reduction by Locally Linear Embedding Roweis, S. T. Science, Vol. 290, Issue 5500 https://doi.org/10.1126/science.290.5500.2323	journal	December 2000
Maximum-a-Posteriori Estimation with Bayesian Confidence Regions Pereyra, Marcelo SIAM Journal on Imaging Sciences, Vol. 10, Issue 1 https://doi.org/10.1137/16M1071249	journal	January 2017
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization Peherstorfer, Benjamin; Willcox, Karen; Gunzburger, Max SIAM Review, Vol. 60, Issue 3 https://doi.org/10.1137/16M1082469	journal	January 2018
GAN-Based Priors for Quantifying Uncertainty in Supervised Learning Patel, Dhruv V.; Oberai, Assad A. SIAM/ASA Journal on Uncertainty Quantification, Vol. 9, Issue 3 https://doi.org/10.1137/20M1354210	journal	January 2021
Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy Sharma, Sanjib Annual Review of Astronomy and Astrophysics, Vol. 55, Issue 1 https://doi.org/10.1146/annurev-astro-082214-122339	journal	August 2017
An Overview of Bayesian Methods for Neural Spike Train Analysis Chen, Zhe Computational Intelligence and Neuroscience, Vol. 2013 https://doi.org/10.1155/2013/251905	journal	January 2013
Nonlinear Component Analysis as a Kernel Eigenvalue Problem Schölkopf, Bernhard; Smola, Alexander; Müller, Klaus-Robert Neural Computation, Vol. 10, Issue 5 https://doi.org/10.1162/089976698300017467	journal	July 1998
A Practical Bayesian Framework for Backpropagation Networks MacKay, David J. C. Neural Computation, Vol. 4, Issue 3 https://doi.org/10.1162/neco.1992.4.3.448	journal	May 1992
Optimal experiment design for model selection in biochemical networks Vanlier, Joep; Tiemann, Christian A.; Hilbers, Peter AJ BMC Systems Biology, Vol. 8, Issue 1 https://doi.org/10.1186/1752-0509-8-20	journal	January 2014
Bayesian inversion whispers Glinsky, Michael E.; Haase, Mark C.; Charoing, Valerie The Leading Edge, Vol. 27, Issue 5 https://doi.org/10.1190/1.2919583	journal	May 2008
Constrained least-squares spectral analysis: Application to seismic data Puryear, Charles I.; Portniaguine, Oleg N.; Cobos, Carlos M. GEOPHYSICS, Vol. 77, Issue 5 https://doi.org/10.1190/geo2011-0210.1	journal	September 2012
Nested sampling for general Bayesian computation Skilling, John Bayesian Analysis, Vol. 1, Issue 4 https://doi.org/10.1214/06-ba127	journal	December 2006
A survey of cross-validation procedures for model selection Arlot, Sylvain; Celisse, Alain Statistics Surveys, Vol. 4, Issue 0 https://doi.org/10.1214/09-ss054	journal	January 2010
A framework for adaptive MCMC targeting multimodal distributions Pompe, Emilia; Holmes, Chris; Łatuszyński, Krzysztof The Annals of Statistics, Vol. 48, Issue 5 https://doi.org/10.1214/19-aos1916	journal	October 2020
On Information and Sufficiency Kullback, S.; Leibler, R. A. The Annals of Mathematical Statistics, Vol. 22, Issue 1 https://doi.org/10.1214/aoms/1177729694	journal	March 1951
Bayesian Inference for a Covariance Matrix Leonard, Tom; Hsu, John S. J. The Annals of Statistics, Vol. 20, Issue 4 https://doi.org/10.1214/aos/1176348885	journal	December 1992
Bayesian Design of Diagnostics: Case Studies for Wendelstein 7-X Dreier, H.; Dinklage, A.; Fischer, R. Fusion Science and Technology, Vol. 50, Issue 2 https://doi.org/10.13182/FST06-A1244	journal	August 2006
Fresnel zone plate development for x-ray radiography of hydrodynamic instabilities at the National Ignition Facility Do, Alexandre; Pickworth, Louisa A.; Kozioziemski, Bernard J. Applied Optics, Vol. 59, Issue 34 https://doi.org/10.1364/ao.408569	journal	January 2020
Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling Vrugt, J. A.; ter Braak, C. J. F.; Diks, C. G. H. International Journal of Nonlinear Sciences and Numerical Simulation, Vol. 10, Issue 3 https://doi.org/10.1515/ijnsns.2009.10.3.273	journal	January 2009
An Introduction to Deep Reinforcement Learning François-Lavet, Vincent; Henderson, Peter; Islam, Riashat Foundations and Trends® in Machine Learning, Vol. 11, Issue 3-4 https://doi.org/10.1561/2200000071	journal	January 2018
PyMC : Bayesian Stochastic Modelling in Python Patil, Anand; Huard, David; Fonnesbeck, Christopher Journal of Statistical Software, Vol. 35, Issue 4 https://doi.org/10.18637/jss.v035.i04	journal	January 2010
Stan : A Probabilistic Programming Language Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D. Journal of Statistical Software, Vol. 76, Issue 1 https://doi.org/10.18637/jss.v076.i01	journal	January 2017
A guide to Bayesian model selection for ecologists Hooten, M. B.; Hobbs, N. T. Ecological Monographs, Vol. 85, Issue 1 https://doi.org/10.1890/14-0661.1	journal	February 2015
UMAP: Uniform Manifold Approximation and Projection McInnes, Leland; Healy, John; Saul, Nathaniel Journal of Open Source Software, Vol. 3, Issue 29 https://doi.org/10.21105/joss.00861	journal	September 2018
Just Another Gibbs Sampler (JAGS) Depaoli, Sarah; Clifton, James P.; Cobb, Patrice R. Journal of Educational and Behavioral Statistics, Vol. 41, Issue 6 https://doi.org/10.3102/1076998616664876	journal	September 2016
A 3D Bayesian Computed Tomography Reconstruction Algorithm with Gauss-Markov-Potts Prior Model and its Application to Real Data Chapdelaine, Camille; Mohammad-Djafari, Ali; Gac, Nicolas Fundamenta Informaticae, Vol. 155, Issue 4 https://doi.org/10.3233/fi-2017-1591	journal	September 2017
Variational Bayesian Approach in Model-Based Iterative Reconstruction for 3D X-Ray Computed Tomography with Gauss-Markov-Potts Prior Chapdelaine, Camille; Mohammad-Djafari, Ali; Gac, Nicolas The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering https://doi.org/10.3390/proceedings2019033004	conference	November 2019
Bilby: A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy Ashton, Gregory; Hübner, Moritz; Lasky, Paul D. The Astrophysical Journal Supplement Series, Vol. 241, Issue 2 https://doi.org/10.3847/1538-4365/ab06fc	journal	April 2019
A new data mining approach to predicting matrix condition numbers Xu, Shuting; Zhang, Jun Communications in Information and Systems, Vol. 4, Issue 4 https://doi.org/10.4310/cis.2004.v4.n4.a4	journal	January 2004
Probabilistic programming in Python using PyMC3 Salvatier, John; Wiecki, Thomas V.; Fonnesbeck, Christopher PeerJ Computer Science, Vol. 2 https://doi.org/10.7717/peerj-cs.55	journal	January 2016

Figures / Tables (25)

Similar Records

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Journal Article · Tue Sep 06 00:00:00 EDT 2022 · ACS Nano · OSTI ID:1961589

Related Subjects

47 OTHER INSTRUMENTATION
Artificial neural networks
Bayesian inference
Bayesian statistics
Data analysis
High energy density physics
Machine learning
Neutron spectra
Plasma confinement
Statistical analysis

Advanced data analysis in inertial confinement fusion and high energy density physics

Citation Formats

References (240)

Figures / Tables (25)

Similar Records

Related Subjects