WIEN-INR: Neural Representation for Lossless Scientific Data Compression
bravo_abad · x · 2026-08-25
Scientific instruments generate data faster than we can store, but compressing it risks losing small structures that often hold critical physical information—unlike photography where such details are expendable. Yuan Ni and coauthors introduce WIEN-INR, a neural representation designed specifically for this domain. It replaces massive voxel arrays with a neural network learning a continuous mapping from coordinates to signal values. Since small neural networks tend to learn smooth, low-frequency structures first—potentially discarding scientifically meaningful high-frequency features—WIEN-INR addresses this limitation by decomposing the measurement.
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