Using Sparse Gaussian Processes for Predicting Robust Inertial Confinement Fusion Implosion Yields

TL;DR

This paper demonstrates the use of Sparse Gaussian Processes to accurately predict inertial confinement fusion yields, effectively handling uncertainty, high-dimensional data, and goal-specific optimization in fusion experiment design.

Contribution

It introduces an advanced Sparse Gaussian Process algorithm tailored for ICF yield prediction, capable of uncertainty decomposition and cost-sensitive learning in high-dimensional spaces.

Findings

GPz can decompose prediction uncertainty into data and shot-to-shot variations.

The method enables goal-specific, cost-sensitive optimization.

It is fast and effective in high-dimensional parameter spaces.

Abstract

Here we present the application of an advanced Sparse Gaussian Process based machine learning algorithm to the challenge of predicting the yields of inertial confinement fusion (ICF) experiments. The algorithm is used to investigate the parameter space of an extremely robust ICF design for the National Ignition Facility, the `Simplest Design'; deuterium-tritium gas in a plastic ablator with a Gaussian, Planckian drive. In particular we show that i) GPz has the potential to decompose uncertainty on predictions into uncertainty from lack of data and shot-to-shot variation, ii) permits the incorporation of science-goal specific cost-sensitive learning e.g. focussing on the high-yield parts of parameter space and iii) is very fast and effective in high dimensions.