arXiv:2606.14353v1 Announce Type: new Abstract: Error-bounded lossy compression is a fundamental technique for managing the rapidly growing volumes of scientific data produced by modern simulations and observational instruments. Most state-of-the-art-compressors follow a prediction-residual paradigm, where compression effectiveness depends on the quality of the predictor: more accurate predictions generate smaller residuals that are easier to compress. This observation raises a question: can modern machine learning models serve as superior predictors for scientific data compression? Answering
The explosion of scientific data generated by simulations and instruments, coupled with advancements in deep neural networks, makes exploring ML-driven compression techniques critically timely for data management.
Improving data compression efficiency for very large scientific datasets directly impacts the feasibility and cost of storing, transmitting, and processing critical research information, affecting all data-intensive scientific fields.
This research explores a shift towards using sophisticated AI models as core components in data compression, potentially accelerating scientific discovery by making massive datasets more manageable.
- · AI/ML researchers
- · Supercomputing centers
- · Scientific research institutions
- · Cloud storage providers
- · Traditional data compression algorithm developers (if not adapting)
- · Organizations with legacy data infrastructure
More efficient storage and transfer of large scientific datasets will become possible.
Accelerated scientific research and discovery due to easier access and processing of complex data, particularly in fields like climate modeling, astrophysics, and drug discovery.
The development of new AI-specific hardware optimized for compression tasks, leading to further integration of AI into fundamental computing infrastructure.
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Read at arXiv cs.LG