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Multivariate Scientific Data Compression with Learned Cross-Variable Latent Decorrelation and Autoregressive Entropy Modeling — arXiv:2608.30262

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.30262
License (as archived): CC0-1.0
Archived: 2026-10-17T08:37:08.025Z
SHA-256 of the archived text: 90a00fdbf3315252945402039944c0e895de4a2463dad297380b19b49f5210bc
Stable URL of this page: https://metraipolis.com/documento/dc_eee882ca2a34

How to cite

Multivariate Scientific Data Compression with Learned Cross-Variable Latent Decorrelation and Autoregressive Entropy Modeling — arXiv:2608.30262 — arXiv (en), https://arxiv.org/abs/2608.30262 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eee882ca2a34 (Metraipolis archive dc_eee882ca2a34, sha256 90a00fdbf3315252945402039944c0e895de4a2463dad297380b19b49f5210bc)

BibTeX

@misc{dceee882ca2a34,
  title = {Multivariate Scientific Data Compression with Learned Cross-Variable Latent Decorrelation and Autoregressive Entropy Modeling — arXiv:2608.30262},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.30262},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eee882ca2a34; sha256 90a00fdbf3315252945402039944c0e895de4a2463dad297380b19b49f5210bc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.30262. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Scientific simulations generate collections of physical fields with heterogeneous statistics and dependencies, yet learned compressors often encode those fields independently or rely on a shared encoder without explicitly modeling the structure that remains in latent space. We present CAESAR-LDAR, an error-controlled multivariate learned compressor that augments a shared CAESAR-V backbone with two complementary mechanisms: a trainable orthogonal transform that reorganizes dependence across aligned latent channels, and a causal autoregressive hierarchical prior that captures local spatial structure left after transformation. Orthogonality is maintained through a matrix-exponential parameterization, making the transform exactly invertible without an additional penalty. A common residual-correction stage is applied uniformly to all variants to enforce the requested reconstruction tolerance. Experiments across combustion, climate, and turbulence data show that the two mechanisms are useful in different regimes. Latent decorrelation helps most when substantial linear cross-channel dependence survives the nonlinear encoder, whereas autoregressive modeling remains effective when the remaining structure is primarily local or spatial. Their combination provides the strongest or near-strongest rate-distortion performance across the evaluated datasets. The global transform adds little computational overhead, while autoregressive coding introduces a larger throughput tradeoff. More broadly, the results suggest a practical design principle for multivariate scientific compression: exploit global cross-channel dependence when it is measurably present in latent space, and use local probabilistic context as a complementary mechanism across a wider range of data regimes.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.30262 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T08:13:26.802Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).