Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design

TL;DR

This paper applies sparse matrix decomposition and machine learning to identify key physical relationships in inertial confinement fusion experiments, aiming to improve experimental design and simulation accuracy.

Contribution

It introduces a novel combination of sparse principal component analysis and random forest modeling to uncover important design variables influencing fusion yield.

Findings

Sparse components relate to physical origins like laser and capsule.

Variables such as pulse steps significantly impact yield.

RF surrogate models perform comparably with fewer variables.

Abstract

A sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulations codes are used to model ICF implosions, these tools contain necessary numerical approximation but miss physical processes that limit predictive capability. Identification of relationships between controllable design inputs to ICF experiments and measurable outcomes (e.g. yield, shape) from performed experiments can help guide the future design of experiments and development of simulation codes, to potentially improve the accuracy of the computational models used to simulate ICF experiments. We use sparse matrix decomposition methods to identify clusters of a few related design variables. Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule). A variable importance analysis finds that in addition to variables highly correlated with neutron yield such as picket power and laser energy, variables that represent a dramatic change of the ICF design such as number of pulse steps are also very important. The obtained sparse components are then used to train a random forest (RF) surrogate for predicting total yield. The RF performance on the training and testing data compares with the performance of the RF surrogate trained using all design variables considered. This work is intended to inform design changes in future ICF experiments by augmenting the expert intuition and simulations results.