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End-to-end demonstration of an autoencoder compression algorithm for plasma ion data from the MMS/FPI space instrument. Accompanies the publications da Silva et al., Frontiers in Astronomy and Space Sciences (2023) and da Silva et al., NeurIPS (2022)

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From Particles to Fluids: Dimensionality Reduction for Non-Maxwellian Plasma Velocity Distributions Validated in the Fluid Context

This code accompanies two papers: a short conference proceedings paper to the NeurIPS 2022 conference, and a full-length version of that paper published early the following year in Frontiers in Astronomy and Space Sciences. The title of the NeurIPS paper is above, and the full-length paper is titled "The Impact of Dimensionality Reduction of Ion Counts Distributions on Preserving Moments with Applications to Data Compression".

This code trains a learned, patch-based, dimensionality reductive method for plasma ion counts distributions using data from the MMS satellite mission's FPI/DIS instrument. It requires data from MMS FPI/DIS, available for free online at the MMS Science Data Center.

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The author can be reached at [email protected] or [email protected].

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End-to-end demonstration of an autoencoder compression algorithm for plasma ion data from the MMS/FPI space instrument. Accompanies the publications da Silva et al., Frontiers in Astronomy and Space Sciences (2023) and da Silva et al., NeurIPS (2022)

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