Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-Energy Density Hohlraum Design Using Forward and Inverse Deep
  Neural Networks   We present a study of using machine learning to enhance hohlraum design for
opacity measurement experiments. For opacity experiments we desire a hohlraum
that, when its interior walls are illuminated by theNational Ignition Facility
(NIF) lasers, will produce a high radiation flux that heats a central sample to
a temperature that is constant over a measurement time window. Given a baseline
hohlraum design and a computational model, we train a deep neural network to
predict the time evolution of the radiation temperature as measured by the
Dante diagnostic. This enables us to rapidly explore design space and determine
the effect of adjusting design parameters. We also construct an "inverse"
machine learning model that predicts the design parameters given a desired time
history of radiation temperature. Calculations using the machine learning model
demonstrate that improved performance over the baseline hohlraum would reduce
uncertainties in experimental opacity measurements.

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The categories of this paper are: q-alg
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio