arXiv:2608.30262v1 Announce Type: new
Abstract: Scientific simulations generate collections of physical fields with heterogeneous statistics and dependencies, yet learned compressors often encode tho...
By Liangji Zhu, Anand Rangarajan, Sanjay Ranka
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.
By Chengjie Hong, Feixiang He, Yiheng Zeng, Lulu Kang, He Wang
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.
By Surya Majumder, Liangji Zhu, Sanjay Ranka, Anand Rangarajan
arXiv:2604. 01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation.
By Zeyu Xia, Tyler Kim, Trevor Reed, Judy Fox, Geoffrey Fox, Adam Szczepaniak
arXiv:2604. 18801v2 Announce Type: replace Abstract: Scientific particle simulations in cosmology, molecular dynamics, and fluid dynamics produce large-scale datasets whose storage, movement, and analysis increasingly rely on lossy compression.
By Congrong Ren, Sheng Di, Katrin Heitmann, Franck Cappello, Hanqi Guo
arXiv:2606. 00635v1 Announce Type: new Abstract: Modern VAEs are rarely trained with the pointwise likelihood implied by the standard $\beta$-VAE objective.
By Giorgio Strano, Luca Cerovaz, Michele Mancusi, Tommaso Mencattini, Emanuele Rodol\`a