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Title: Parameter inference with deep jointly informed neural networks

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3]
  1. Lawrence Livermore National Laboratory Livermore California, Department of Nuclear EngineeringTexas A&, University College Station Texas
  2. Lawrence Livermore National Laboratory Livermore California
  3. Department of Aerospace and Mechanical EngineeringUniversity of Notre Dame Notre Dame Indiana
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1546092
Grant/Contract Number:  
DE‐AC52‐07NA27344
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Statistical Analysis and Data Mining
Additional Journal Information:
Journal Name: Statistical Analysis and Data Mining Journal Volume: 12 Journal Issue: 6; Journal ID: ISSN 1932-1864
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States
Language:
English

Citation Formats

Humbird, Kelli D., Peterson, J. Luc, and McClarren, Ryan G. Parameter inference with deep jointly informed neural networks. United States: N. p., 2019. Web. doi:10.1002/sam.11435.
Humbird, Kelli D., Peterson, J. Luc, & McClarren, Ryan G. Parameter inference with deep jointly informed neural networks. United States. doi:10.1002/sam.11435.
Humbird, Kelli D., Peterson, J. Luc, and McClarren, Ryan G. Fri . "Parameter inference with deep jointly informed neural networks". United States. doi:10.1002/sam.11435.
@article{osti_1546092,
title = {Parameter inference with deep jointly informed neural networks},
author = {Humbird, Kelli D. and Peterson, J. Luc and McClarren, Ryan G.},
abstractNote = {},
doi = {10.1002/sam.11435},
journal = {Statistical Analysis and Data Mining},
number = 6,
volume = 12,
place = {United States},
year = {2019},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1002/sam.11435

Citation Metrics:
Cited by: 2 works
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Works referenced in this record:

The high velocity, high adiabat, “Bigfoot” campaign and tests of indirect-drive implosion scaling
journal, May 2018

  • Casey, D. T.; Thomas, C. A.; Baker, K. L.
  • Physics of Plasmas, Vol. 25, Issue 5
  • DOI: 10.1063/1.5019741

On the limited memory BFGS method for large scale optimization
journal, August 1989

  • Liu, Dong C.; Nocedal, Jorge
  • Mathematical Programming, Vol. 45, Issue 1-3
  • DOI: 10.1007/BF01589116

Deep Neural Network Initialization With Decision Trees
journal, May 2019

  • Humbird, Kelli D.; Peterson, J. Luc; Mcclarren, Ryan G.
  • IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, Issue 5
  • DOI: 10.1109/TNNLS.2018.2869694

Least angle regression
journal, April 2004


Interpreting inertial fusion neutron spectra
journal, February 2016


Analysis of the neutron time-of-flight spectra from inertial confinement fusion experiments
journal, November 2015

  • Hatarik, R.; Sayre, D. B.; Caggiano, J. A.
  • Journal of Applied Physics, Vol. 118, Issue 18
  • DOI: 10.1063/1.4935455

Hedonic housing prices and the demand for clean air
journal, March 1978

  • Harrison, David; Rubinfeld, Daniel L.
  • Journal of Environmental Economics and Management, Vol. 5, Issue 1
  • DOI: 10.1016/0095-0696(78)90006-2

Random Forests
journal, January 2001


Monte Carlo sampling methods using Markov chains and their applications
journal, April 1970


BART: Bayesian additive regression trees
journal, March 2010

  • Chipman, Hugh A.; George, Edward I.; McCulloch, Robert E.
  • The Annals of Applied Statistics, Vol. 4, Issue 1
  • DOI: 10.1214/09-AOAS285

X-ray shadow imprint of hydrodynamic instabilities on the surface of inertial confinement fusion capsules by the fuel fill tube
journal, March 2017


Three-dimensional HYDRA simulations of National Ignition Facility targets
journal, May 2001

  • Marinak, M. M.; Kerbel, G. D.; Gentile, N. A.
  • Physics of Plasmas, Vol. 8, Issue 5
  • DOI: 10.1063/1.1356740