Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning

TL;DR

This paper uses machine learning, specifically random forest regression, to analyze how design parameters influence outcomes in inertial confinement fusion experiments, aiding in better experiment design and simulation accuracy.

Contribution

It introduces a machine learning approach to identify key design parameters affecting ICF outcomes, enhancing understanding beyond traditional expert analysis.

Findings

RF models predict ICF outcomes with high accuracy

Feature importance reveals key physical relationships

Uncertainty assessment improves model reliability

Abstract

Building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimental design changes have on implosion performance. While simulation codes are used to model ICF implosions, incomplete physics and the need for approximations deteriorate their predictive capability. Identification of relationships between controllable design inputs and measurable outcomes can help guide the future design of experiments and development of simulation codes, which can potentially improve the accuracy of the computational models used to simulate ICF implosions. In this paper, we leverage developments in machine learning (ML) and methods for ML feature importance/sensitivity analysis to identify complex relationships in ways that are difficult to process using expert judgment alone. We present work using random forest (RF) regression for prediction of yield, velocity, and other experimental outcomes given a suite of design parameters, along with an assessment of important relationships and uncertainties in the prediction model. We show that RF models are capable of learning and predicting on ICF experimental data with high accuracy, and we extract feature importance metrics that provide insight into the physical significance of different controllable design inputs for various ICF design configurations. These results can be used to augment expert intuition and simulation results for optimal design of future ICF experiments.