arXiv AI
Jun 6
Residual Modeling for High-Fidelity Learned Compression of Scientific Data
arXiv:2606. 05389v1 Announce Type: new Abstract: Lossy compression is essential for massive spatiotemporal data from scientific simulations.
EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database
arXiv:2607. 18187v1 Announce Type: cross Abstract: Large-scale scientific simulations generate volumetric data at rates that far outpace advances in storage and network bandwidth, making effective lossy compression increasingly critical.
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arXiv Machine Learning
Jun 15
Can Deep Neural Networks Improve Compression of Very Large Scientific Data?
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.
arXiv Machine Learning
Sep 22
Neural Residual Modeling for Scientific Data Compression under Guaranteed Error Bounds
The paper introduces a new compression pipeline for scientific simulation data that combines Residual Vector Quantization (RVQ) with a U‑Net post‑processing network to correct pixel‑space residuals, followed by a Guaranteed Autoencoder (GAE) that enforces block‑wise error bounds. The U‑Net is trained to predict spatially structured residuals, addressing limitations of latent‑space only approaches. Experiments on S3D, JHTDB, and E3SM datasets show improved NRMSE and compression ratios compared to RVQ alone while maintaining strict error guarantees.
arXiv Machine Learning
Jul 2
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
arXiv:2607. 01164v1 Announce Type: new Abstract: Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint.
arXiv AI
Jun 16
Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modeling
arXiv:2606. 15959v1 Announce Type: cross Abstract: Neural networks are used as generative surrogate models for scientific discovery, which are trainable approximations of scientific simulations.
arXiv AI
Aug 17
Fixed-Budget Gaussian Volume Encoding with Structure-Aware Allocation
arXiv:2608. 14112v1 Announce Type: cross Abstract: Scientific simulations often produce scalar volumes faster than they can be stored, transferred, and loaded, while in situ reduction must use only a limited share of simulation resources.
arXiv Machine Learning
Sep 25
Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers
The paper investigates why latent neural surrogate solvers, which compress physical system dynamics into a lower‑dimensional space, often fail during long‑horizon autoregressive rollouts. It demonstrates that training the latent representation only for reconstruction leads to instability, and proposes a set of training interventions—Koopman operator learning, Hamming noise injection, and multi‑step rollout fine‑tuning—that align the latent space with long‑horizon forecasting. These interventions reduce long‑rollout error by about 40 % and achieve accuracy comparable to full‑resolution models while using far fewer floating‑point operations and GPU memory, enabling stable extrapolation in mesoscale crystal‑plasticity simulations of high‑cycle fatigue.
arXiv Machine Learning
Jul 14
Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data
arXiv:2607. 11883v1 Announce Type: new Abstract: Compression is fundamental to intelligence.
arXiv AI
Aug 14
SAFE-SVD: Sensitivity-Aware Fidelity-Enforcing SVD for Physics Foundation Models
arXiv:2605. 17985v2 Announce Type: replace-cross Abstract: We propose a new method for compressing physics foundation models (PFMs) which is a new trend in AI for Science.
arXiv Machine Learning
Jun 30
Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets
arXiv:2606. 29975v1 Announce Type: new Abstract: Atomistic machine learning datasets are increasingly used for training: large immutable snapshots are read repeatedly, shuffled across epochs, staged across clusters' storage systems, and republished as reusable scientific artifacts.
arXiv AI
Jun 9
End-to-End Context Compression at Scale
arXiv:2606. 09659v1 Announce Type: cross Abstract: Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length.
Hugging Face Trending Papers
Jun 29
Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets
Atomistic machine learning datasets are increasingly used for training: large immutable snapshots are read repeatedly, shuffled across epochs, staged across clusters' storage systems, and republished as reusable scientific artifacts. This workload differs from interactive scientific curation, where mutable records and ad hoc inspection are often more important than random indexed throughput.