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Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design
Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule).
Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning
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.
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