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We propose an unsupervised machine-learning checkpoint-restart (CR) lossyalgorithm for particle-in-cell (PIC) algorithms using Gaussian mixtures (GM).The algorithm features a particle compression stage and a particlereconstruction stage, where a continuum particle distribution function isconstructed and resampled, respectively. To guarantee fidelity of the CRprocess, we ensure the exact preservation of charge, momentum, and energy forboth compression and reconstruction stages, everywhere on the mesh. We alsoensure the preservation of Gauss' law after particle reconstruction. As aresult, the GM CR algorithm is shown to provide a clean, conservative restartcapability while potentially affording orders of magnitude savings ininput/output requirements. We demonstrate the algorithm using a recentlydeveloped exactly energy- and charge-conserving PIC algorithm on physicalproblems of interest, with compression factors $\gtrsim75$ with no appreciableimpact on the quality of the restarted dynamics.

An unsupervised machine-learning checkpoint-restart algorithm using Gaussian mixtures for particle-in-cell simulations