High-Energy Density Hohlraum Design Using Forward and Inverse Deep Neural Networks

TL;DR

This paper employs deep neural networks to optimize hohlraum designs for opacity experiments, enabling rapid exploration and inverse design to improve experimental accuracy.

Contribution

It introduces a novel application of deep learning for both forward prediction and inverse design of hohlraum parameters in high-energy density physics.

Findings

Neural network accurately predicts radiation temperature evolution.

Inverse model successfully identifies design parameters for target temperature profiles.

Improved hohlraum designs can reduce uncertainties in opacity measurements.

Abstract

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.