Parameter inference with deep jointly informed neural networks
Abstract
Abstract A common challenge in modeling inertial confinement fusion (ICF) experiments with computer simulations is that many of the simulation inputs are unknown and cannot be directly measured. Often, parameters that are measured in the experiment are used to infer the unknown inputs by solving the inverse problem: finding the set of simulation inputs that result in outputs consistent with the experimental observations. In ICF, this process is often referred to as a “post‐shot analysis.” Post‐shot analyses are challenging as the inverse problem is often highly degenerate, the input parameter space is vast, and simulations are computationally expensive. In this work, deep neural network models equipped with model uncertainty estimates are used to train inverse models, which map directly from output to input space, to find the distribution of post‐shot simulations that are consistent with experimental observations. The inverse model approach is compared to Markov chain Monte Carlo (MCMC) sampling of the forward model, which maps from input to output space, for parameter inference tasks of varying complexity. The inverse models perform best when searching vast parameter spaces for post‐shot simulations that are consistent with a large number of observables, where MCMC sampling can be prohibitively expensive. We demonstrate howmore »
 Authors:

 Lawrence Livermore National Laboratory Livermore California, Department of Nuclear Engineering Texas A&, University College Station Texas
 Lawrence Livermore National Laboratory Livermore California
 Department of Aerospace and Mechanical Engineering University 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 19321864
 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. https://doi.org/10.1002/sam.11435
Humbird, Kelli D., Peterson, J. Luc, and McClarren, Ryan G.. Thu .
"Parameter inference with deep jointly informed neural networks". United States. https://doi.org/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 = {Abstract A common challenge in modeling inertial confinement fusion (ICF) experiments with computer simulations is that many of the simulation inputs are unknown and cannot be directly measured. Often, parameters that are measured in the experiment are used to infer the unknown inputs by solving the inverse problem: finding the set of simulation inputs that result in outputs consistent with the experimental observations. In ICF, this process is often referred to as a “post‐shot analysis.” Post‐shot analyses are challenging as the inverse problem is often highly degenerate, the input parameter space is vast, and simulations are computationally expensive. In this work, deep neural network models equipped with model uncertainty estimates are used to train inverse models, which map directly from output to input space, to find the distribution of post‐shot simulations that are consistent with experimental observations. The inverse model approach is compared to Markov chain Monte Carlo (MCMC) sampling of the forward model, which maps from input to output space, for parameter inference tasks of varying complexity. The inverse models perform best when searching vast parameter spaces for post‐shot simulations that are consistent with a large number of observables, where MCMC sampling can be prohibitively expensive. We demonstrate how augmenting inverse models with autoencoders enable the inclusion of several dozen observables in the inverse mapping, reducing the degeneracy of the model and improving the accuracy of the post‐shot analysis.},
doi = {10.1002/sam.11435},
journal = {Statistical Analysis and Data Mining},
number = 6,
volume = 12,
place = {United States},
year = {2019},
month = {8}
}
https://doi.org/10.1002/sam.11435
Web of Science
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