arXiv:2606.05389v1 Announce Type: new Abstract: Lossy compression is essential for massive spatiotemporal data from scientific simulations. Learned compressors can achieve high compression ratios at moderate accuracy targets, but their aggregate reconstruction losses do not guarantee accuracy for each block. Existing Guaranteed Autoencoder (GAE) methods add a per-block residual correction by retaining SVD/PCA-style coefficients until the target is met. This works at moderate tolerances, but in the high-fidelity regime with block-level NRMSE from 10^-6 to 10^-4, the number of retained coefficie
The proliferation of massive scientific datasets from simulations necessitates more efficient compression techniques, and this research addresses a current limitation in high-fidelity data preservation.
Improved high-fidelity learned compression for scientific data is crucial for managing and utilizing the explosion of information in fields like climate modeling, astrophysics, and drug discovery without compromising accuracy.
This advancement promises to enable more effective storage, transmission, and analysis of highly accurate scientific data, pushing the boundaries of what is possible with large-scale simulations.
- · Scientific research institutions
- · Cloud storage providers
- · Data compression software developers
- · High-performance computing (HPC) centers
- · Current inefficient data storage methods
- · Researchers limited by data transfer bottlenecks
More sophisticated scientific simulations can be run and preserved due to reduced data footprint.
Faster interdisciplinary collaboration and data sharing become possible, accelerating discovery.
The development of new AI models trained on previously inaccessible high-fidelity datasets could lead to breakthroughs in various scientific domains.
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Read at arXiv cs.AI