Modern scientific simulations, observations, and large-scale experiments generate data at volumes that often exceed the limits of storage, processing, and analysis. This challenge drives the development of data reduction methods that efficiently manage massive datasets while preserving essential physical features and quantities of interest. In many scientific workflows, it is also crucial to enable data recovery from compressed representations - a task known as super-resolution - with guarantees on the preservation of key physical characteristics. A notable example is checkpointing and restarting, which is essential for long-running simulations to recover from failures, resume after interruptions, or examine intermediate results. In this work, we introduce a novel framework for scientific data compression and super-resolution, grounded in recent advances in learning exponential families. Our method preserves and quantifies uncertainty in physical quantities of interest and supports flexible trade-offs between compression ratio and reconstruction fidelity.
Exploring Autoencoder-based Error-bounded Compression for Scientific Data
University of California, Riverside（加州大学河滨分校）；Argonne National Laboratory（阿贡国家实验室）；Washington State University（华盛顿州立大学）；Missouri University of Science and Technology（密苏里科技大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
University of California, Riverside（加州大学河滨分校）；Argonne National Laboratory（阿贡国家实验室）；University of Kentucky（肯塔基大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Florida State University（佛罗里达州立大学）；Indiana University Bloomington（印第安纳大学伯明顿分校）
GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders
Computer, Electrical and Mathematical Sciences and Engineering Division, KAUST（卡奥斯特大学计算机、电气与数学科学与工程分校）；Department of Informatics and Telecommunications, University of Peloponnese（希腊皮埃尼亚斯大学信息与电信系）；Physical Science and Engineering Division, KAUST（卡奥斯特大学物理科学与工程分校）
DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction
University of North Carolina at Charlotte（北卡罗来纳州立大学）；Argonne National Laboratory（阿贡国家实验室）；University of Delaware（德雷塞尔大学）；Lawrence Berkeley National Laboratory（伯克利国家实验室）
In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization
Applied Mathematics, University of Washington, Seattle（华盛顿大学应用数学系，西雅图）；Applied Mathematics, University of Colorado, Boulder（科罗拉多大学应用数学系，伯尔德）；Ann and H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado, Boulder（科罗拉多大学伯尔德分校安与H.J. 塞梅德航空航天工程科学系）
Robustness of deep learning algorithms in astronomy -- galaxy morphology studies
Fermi National Accelerator Laboratory（费米国家加速器实验室）；Space Telescope Science Institute（空间望远镜科学研究所）；Argonne National Laboratory（阿贡国家实验室）；Kavli Institute for Cosmological Physics（宇宙物理研究所）；University of Chicago（芝加哥大学）
