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A sustainable burn platform through inertial confinement fusion (ICF) hasbeen an ongoing challenge for over 50 years. Mitigating engineering limitationsand improving the current design involves an understanding of the complexcoupling of physical processes. While sophisticated simulations codes are usedto model ICF implosions, these tools contain necessary numerical approximationbut miss physical processes that limit predictive capability. Identification ofrelationships between controllable design inputs to ICF experiments andmeasurable outcomes (e.g. yield, shape) from performed experiments can helpguide the future design of experiments and development of simulation codes, topotentially improve the accuracy of the computational models used to simulateICF experiments. We use sparse matrix decomposition methods to identifyclusters of a few related design variables. Sparse principal component analysis(SPCA) identifies groupings that are related to the physical origin of thevariables (laser, hohlraum, and capsule). A variable importance analysis findsthat in addition to variables highly correlated with neutron yield such aspicket power and laser energy, variables that represent a dramatic change ofthe ICF design such as number of pulse steps are also very important. Theobtained sparse components are then used to train a random forest (RF)surrogate for predicting total yield. The RF performance on the training andtesting data compares with the performance of the RF surrogate trained usingall design variables considered. This work is intended to inform design changesin future ICF experiments by augmenting the expert intuition and simulationsresults.

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