A single autoencoder trained on 6,376 simulation volumes compresses unseen scientific volumes at 2,000–10,000× compression with higher reported fidelity than prior compressors, with adjustable rate at inference.
Interactivespatio-temporalexploration of massive time-varying rectilinear scalar volumes based on a variable bit-rate sparse representation over learned dictionaries.Comput
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EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database
A single autoencoder trained on 6,376 simulation volumes compresses unseen scientific volumes at 2,000–10,000× compression with higher reported fidelity than prior compressors, with adjustable rate at inference.