Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data

TL;DR

This paper introduces a machine learning approach that combines simulation and experimental data to create more accurate predictive models for inertial confinement fusion experiments, reducing prediction errors and aiding experimental design.

Contribution

The work presents a novel transfer learning-based method to integrate simulation and experimental data into a unified predictive model for ICF, improving accuracy over traditional simulations.

Findings

Models predict experimental outcomes with less than 10% error.

Transfer learning significantly enhances model accuracy.

Method enables data-driven optimization of ICF experiments.

Abstract

The design space for inertial confinement fusion (ICF) experiments is vast and experiments are extremely expensive. Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. However, ICF multiphysics codes must make simplifying assumptions, and thus deviate from experimental measurements for complex implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. In this work, we describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model. This method leverages a machine learning technique called transfer learning, the process of taking a model trained to solve one task, and partially retraining it on a sparse dataset to solve a different, but related task. In the context of ICF design, neural network models trained on large simulation databases and partially retrained on experimental data, producing models that are far more accurate than simulations alone. We demonstrate improved model performance for a range of ICF experiments at the National Ignition Facility, and predict the outcome of recent experiments with less than ten percent error for several key observables. We discuss how the methods might be used to carry out a data-driven experimental campaign to optimize performance, illustrating the key product -- models that become increasingly accurate as data is acquired.