×

zbMATH — the first resource for mathematics

Prediction uncertainties beyond the range of experience: a case study in inertial confinement fusion implosion experiments. (English) Zbl 1430.62263
MSC:
62P35 Applications of statistics to physics
62-08 Computational methods for problems pertaining to statistics
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] J. S. Armstrong, Combining forecasts, in Principles of Forecasting, Springer, New York, 2001, pp. 417–439.
[2] M. J. Bayarri, J. O. Berger, R. Paulo, J. Sacks, J. A. Cafeo, J. Cavendish, C. Lin, and J. Tu, A framework for validation of computer models, Technometrics, 49 (2007), pp. 138–154.
[3] T. R. Boehly, D. L. Brown, R. S. Craxton, R. L. Keck, J. P. Knauer, J. H. Kelly, T. J. Kessler, S. A. Kumpan, S. J. Loucks, S. A. Letzring, F. J. Marshall, R. L. McCrory, S. F. B. Morse, W. Seka, J. M. Soures, and C. P. Verdon, Initial performance results of the OMEGA laser system, Optics Commun., 133 (1997), pp. 495–506.
[4] J. Brynjarsdóttir and A. O’Hagan, Learning about physical parameters: The importance of model discrepancy, Inverse Problems, 30 (2014), 114007.
[5] T. G. Dietterich, Ensemble methods in machine learning, in International Workshop on Multiple Classifier Systems, Springer, New York, 2000, pp. 1–15.
[6] G. Dimonte, Spanwise homogeneous buoyancy-drag model for Rayleigh–Taylor mixing and experimental evaluation, Phys. Plasmas, 7 (2000), pp. 2255–2269.
[7] J. R. Gattiker, Gaussian Process Models for Simulation Analysis (GPM/SA) Command, Function, and Data Structure Reference, Tech. report, Los Alamos National Laboratory, Los Alamos, NM, 2008.
[8] T. Gneiting, F. Balabdaoui, and A. E. Raftery, Probabilistic forecasts, calibration and sharpness, J. R. Stat. Soc. Ser. B Stat. Methodol., 69 (2007), pp. 243–268. · Zbl 1120.62074
[9] D. Higdon, J. Gattiker, B. Williams, and M. Rightley, Computer model calibration using high-dimensional output, J. Amer. Statist. Assoc., 103 (2008), pp. 570–583. · Zbl 05564511
[10] J. A. Hoeting, D. Madigan, A. E. Raftery, and C. T. Volinsky, Bayesian model averaging: A tutorial, Statist. Sci., 14 (1999), pp. 382–417.
[11] N. M. Hoffman, G. B. Zimmerman, K. Molvig, H. G. Rinderknecht, M. J. Rosenberg, B. Albright, A. N. Simakov, H. Sio, A. B. Zylstra, M. G. Johnson, F. H. Séguin, J. A. Frenje, C. Li, R. D. Petrasso, D. M. Higdon, G. Srinivasan, V. Y. Glebov, C. Stoeckl, W. Seka, and T. C. Sangster, Approximate models for the ion-kinetic regime in inertial-confinement-fusion capsule implosions, Phys. Plasmas, 22 (2015), 052707.
[12] B. R. Jasny and R. Stone, Prediction and its limits, Science, 355 (2017), pp. 468–469.
[13] G. Kagan and X.-Z. Tang, Thermo-diffusion in inertially confined plasmas, Phys. Lett. A, 378 (2014), pp. 1531–1535.
[14] G. H. Miller, E. I. Moses, and C. R. Wuest, The National Ignition Facility: Enabling fusion ignition for the 21st century, Nuclear Fusion, 44 (2004), S228.
[15] R. H. Moss and S. H. Schneider, Uncertainties in the IPCC TAR: Recommendations to lead authors for more consistent assessment and reporting, in Guidance Papers on the Cross-Cutting Issues of the Third Assessment Report of the IPCC, Intergovernmental Panel on Climate Change, R. Pachauri, T. Taniguchi, and K. Tanaka, eds., Geneva, Switzerland, 2001, pp. 33–51.
[16] H. N. Najm, Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics, in Annual Review of Fluid Mechanics, Annu. Rev. Fluid Mech. 41, Annual Reviews, Palo Alto, CA, 2009, pp. 35–52.
[17] National Research Council, Assessing the Reliability of Complex Models: Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification, National Academies Press, Washington, DC, 2012.
[18] C. Paquette, C. Pelletier, G. Fontaine, and G. Michaud, Diffusion coefficients for stellar plasmas, Astrophys. J. Supplement Ser., 61 (1986), pp. 177–195.
[19] M. Rosenberg, H. Rinderknecht, N. Hoffman, P. Amendt, S. Atzeni, A. Zylstra, C. Li, F. Séguin, H. Sio, M. G. Johnson, J. Frenje, R. Petrasso, V. Y. Glebov, C. Stoeckl, W. Seka, F. Marshall, J. Delettrez, T. Sangster, R. Betti, V. Goncharov, D. Meyerhofer, S. Skupsky, C. Bellei, J. Pino, S. Wilks, G. Kagan, K. Molvig, and A. Nikroo, Exploration of the transition from the hydrodynamic-like to the strongly kinetic regime in shock-driven implosions, Phys. Rev. Lett., 112 (2014), 185001.
[20] J. Smith and K. F. Wallis, A simple explanation of the forecast combination puzzle, Oxford Bull. Econom. Statist., 71 (2009), pp. 331–355.
[21] U.S. Department of Energy, National Nuclear Security Administration, 2015 Review of the inertial confinement fusion and high energy density science portfolio, DOE/NA-0040 (2016), pp. (ii, 9–12), http://www.firefusionpower.org/ICF_HED_Review_Report_2015_Update.pdf, visited on February 2nd, 2017.
[22] V. M. Zhdanov, Transport processes in multicomponent plasma, Plasma Phys. Controlled Fusion, 44 (2002), 2283.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.